diff --git a/.gitignore b/.gitignore
index f6153d3..f933185 100644
--- a/.gitignore
+++ b/.gitignore
@@ -20,6 +20,8 @@ build/
 develop-eggs/
 dist/
 downloads/
+input/
+output/*
 eggs/
 .eggs/
 lib/
@@ -165,4 +167,8 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-.idea/
\ No newline at end of file
+.idea/
+
+# Random inputs and outputs
+random_inputs/
+random_outputs/
\ No newline at end of file
diff --git a/README.md b/README.md
index 59e2721..b2d081c 100644
--- a/README.md
+++ b/README.md
@@ -8,7 +8,33 @@ Text extraction like [PyMuPDF](https://github.com/pymupdf/PyMuPDF), but without
 
 # Installation
 
-You'll need python 3.9+ first.  Then run `pip install pdftext`.
+You'll need python 3.10+ first.  Then run `pip install pdftext`.
+
+# Development
+
+To set up the development environment:
+
+1. Clone the repository:
+```shell
+git clone https://github.com/VikParuchuri/pdftext.git
+cd pdftext
+```
+
+2. Create and activate a virtual environment:
+```shell
+python -m venv .venv
+source .venv/bin/activate  # On Windows use `.venv\Scripts\activate`
+```
+
+3. Install dependencies using UV:
+```shell
+uv pip install -r requirements-dev.txt
+```
+
+4. Run tests:
+```shell
+uv run pytest
+```
 
 # Usage
 
@@ -132,12 +158,14 @@ For the alignment score, I extracted the text, then used the rapidfuzz library t
 
 ## Running benchmarks
 
-You can run the benchmarks yourself.  To do so, you have to first install pdftext manually.  The install assumes you have poetry and Python 3.9+ installed.
+You can run the benchmarks yourself.  To do so, you have to first install pdftext manually.  The install assumes you have Python 3.10+ installed.
 
 ```shell
 git clone https://github.com/VikParuchuri/pdftext.git
 cd pdftext
-poetry install
+python -m venv .venv
+source .venv/bin/activate  # On Windows use `.venv\Scripts\activate`
+uv pip install -r requirements-dev.txt
 python benchmark.py # Will download the benchmark pdfs automatically
 ```
 
diff --git a/input/reliance_shares.pdf b/input/reliance_shares.pdf
new file mode 100644
index 0000000..d3463e2
Binary files /dev/null and b/input/reliance_shares.pdf differ
diff --git a/mypy.ini b/mypy.ini
new file mode 100644
index 0000000..c272707
--- /dev/null
+++ b/mypy.ini
@@ -0,0 +1,25 @@
+[mypy]
+python_version = 3.13
+warn_return_any = True
+warn_unused_configs = True
+disallow_untyped_defs = True
+disallow_incomplete_defs = True
+check_untyped_defs = True
+ignore_missing_imports = True
+# We'll progressively remove these as we fix the errors
+disable_error_code = no-redef,misc,no-any-return
+
+# For third-party libraries without type stubs
+[mypy-pypdfium2.*]
+ignore_missing_imports = True
+
+[mypy-pypdfium2]
+ignore_missing_imports = True
+
+# Exclude root level extract_text.py file
+[mypy-extract_text]
+ignore_errors = True
+
+# Ignore errors in pdftext.tables
+[mypy-pdftext.tables]
+ignore_errors = True 
\ No newline at end of file
diff --git a/output/bbox_images/page_1_visualization.png b/output/bbox_images/page_1_visualization.png
new file mode 100644
index 0000000..a72d287
Binary files /dev/null and b/output/bbox_images/page_1_visualization.png differ
diff --git a/output/reliance_bbox.json b/output/reliance_bbox.json
new file mode 100644
index 0000000..d979d4a
--- /dev/null
+++ b/output/reliance_bbox.json
@@ -0,0 +1 @@
+{"pages": [{"page": 0, "bbox": [0.0, 0.0, 792.0, 612.0], "width": 792, "height": 612, "rotation": 0, "blocks": [{"lines": [{"spans": [{"bbox": [345.0, 80.373291015625, 361.56488037109375, 84.70147705078125], "text": "Class X ", "rotation": 0.0, "font": {"name": "AAAAAB+Arial-BoldMT", "flags": 524292, "color": [255, 255, 255, 255], "size": 1.0, "weight": 0}, "char_start_idx": 0, "char_end_idx": 7, "url": "", "superscript": false, "subscript": false}, {"bbox": [365.171142578125, 80.373291015625, 381.0287780761719, 84.70147705078125], "text": "(Equity)", "rotation": 0.0, "font": {"name": "AAAAAB+Arial-BoldMT", "flags": 524292, "color": [255, 255, 255, 255], "size": 1.0, "weight": 0}, "char_start_idx": 8, "char_end_idx": 15, "url": "", "superscript": false, "subscript": false}, {"bbox": [379.9205017089844, 83.8800048828125, 379.9205017089844, 83.8800048828125], "text": " ", "rotation": 0.0, "font": {"name": "", "flags": 0, "color": [], "size": 1.0, "weight": -1}, "char_start_idx": 16, "char_end_idx": 16, "url": "", "superscript": false, "subscript": false}, {"bbox": [385.1999816894531, 77.61328125, 397.7127990722656, 81.94146728515625], "text": "Class ", "rotation": 0.0, "font": {"name": "AAAAAB+Arial-BoldMT", "flags": 524292, "color": [255, 255, 255, 255], "size": 1.0, "weight": 0}, "char_start_idx": 17, "char_end_idx": 22, "url": "", "superscript": false, "subscript": false}, {"bbox": [396.7875671386719, 81.1199951171875, 396.7875671386719, 81.1199951171875], "text": "\n", "rotation": 0.0, "font": {"name": "", "flags": 0, "color": [], "size": 1.0, "weight": -1}, "char_start_idx": 23, "char_end_idx": 23, "url": "", "superscript": false, "subscript": false}, {"bbox": [396.7875671386719, 81.1199951171875, 396.7875671386719, 81.1199951171875], "text": "\n", "rotation": 0.0, "font": {"name": "", "flags": 0, "color": [], "size": 1.0, "weight": -1}, "char_start_idx": 24, "char_end_idx": 24, "url": "", "superscript": false, "subscript": false}], "bbox": [345.0, 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EUgAAAAAEJZACAAAAICiBFAAAAABBCaQAAAAACEogBQAAAEBQAikAAAAAghJIAQAAABCUQAoAAACAoARSAAAAAAQlkAIAAAAgKIEUAAAAAEEJpAAAAAAISiAFAAAAQFACKQAAAACCEkgBAAAAEJRACgAAAICgBFIAAAAABCWQAgAAACAogRQAAAAAQQmkAAAAAAhKIAUAAABAUAIpAAAAAIISSAEAAAAQlEAKAAAAgKAEUgAAAAAEJZACAAAAICiBFAAAAABBCaQAAAAACEogBQAAAEBQAikAAAAAghJIAQAAABCUQAoAAACAoARSAAAAAAQlkAIAAAAgKIEUAAAAAEEJpAAAAAAISiAFAAAAQFACKQAAAACCEkgBAAAAEJRACgAAAICgBFIAAAAABCWQAgAAACAogRQAAAAAQQmkAAAAAAhKIAUAAABAUAIpAAAAAIISSAEAAAAQlEAKAAAAgKAEUgAAAAAEJZACAAAAICiBFAAAAABBCaQAAAAACEogBQAAAEBQAikAAAAAghJIAQAAABCUQAoAAACAoARSAAAAAAQlkAIAAAAgKIEUAAAAAEEJpAAAAAAISiAFAAAAQFACKQAAAACCEkgBAAAAEJRACgAAAICgBFIAAAAABCWQAgAAACAogRQAAAAAQQmkAAAAAAhKIAUAAABAUAIpAAAAAIISSAEAAAAQlEAKAAAAgKAEUgAAAAAEJZACAAAAICiBFAAAAABBCaQAAAAACEogBQAAAEBQAikAAAAAghJIAQAAABCUQAoAAACAoARSAAAAAAQlkAIAAAAgKIEUAAAAAEEJpAAAAAAISiAFAAAAQFACKQAAAACCEkgBAAAAEJRACgAAAICgBFIAAAAABCWQAgAAACAogRQAAAAAQQmkAAAAAAhKIAUAAABAUAIpAAAAAIISSAEAAAAQlEAKAAAAgKAEUgAAAAAEJZACAAAAICiBFAAAAABBCaQAAAAACEogBQAAAEBQAikAAAAAghJIAQAAABCUQAoAAACAoARSAAAAAAQlkAIAAAAgKIEUAAAAAEEJpAAAAAAISiAFAAAAQFACKQAAAACCEkgBAAAAEJRACgAAAICgBFIAAAAABCWQAgAAACAogRQAAAAAQQmkAAAAAAhKIAUAAABAUAIpAAAAAIISSAEAAAAQlEAKAAAAgKAEUgAAAAAEJZACAAAAICiBFAAAAABBCaQAAAAACEogBQAAAEBQAikAAAAAghJIAQAAABCUQAoAAACAoARSAAAAAAQlkAIAAAAgKIEUAAAAAEEJpAAAAAAISiAFAAAAQFACKQAAAACCEkgBAAAAEJRACgAAAICgBFIAAAAABCWQAgAAACAogRQAAAAAQQmkAAAAAAhKIAUAAABAUAIpAAAAAIISSAEAAAAQlEAKAAAAgKAEUgAAAAAEJZACAAAAICiBFAAAAABBCaQAAAAACEogBQAAAEBQAikAAAAAghJIAQAAABCUQAoAAACAoARSAAAAAAQlkAIAAAAgKIEUAAAAAEEJpAAAAAAISiAFAAAAQFACKQAAAACCEkgBAAAAEJRACgAAAICgBFIAAAAABCWQAgAAACAogRQAAAAAQQmkAAAAAAhKIAUAAABAUAIpAAAAAIISSAEAAAAQlEAKAAAAgKAEUgAAAAAEJZACAAAAICiBFAAAAABBCaQAAAAACEogBQAAAEBQAikAAAAAghJIAQAAABCUQAoAAACAoARSAAAAAAQlkAIAAAAgKIEUAAAAAEEJpAAAAAAISiAFAAAAQFACKQAAAACCEkgBAAAAEJRACgAAAICgBFIAAAAABCWQAgAAACAogRQAAAAAQQmkAAAAAAhKIAUAAABAUAIpAAAAAIISSAEAAAAQlEAKAAAAgKAEUgAAAAAEJZACAAAAICiBFAAAAABBCaQAAAAACEogBQAAAEBQAikAAAAAghJIAQAAABCUQAoAAACAoARSAAAAAAQlkAIAAAAgKIEUAAAAAEEJpAAAAAAISiAFAAAAQFACKQAAAACCEkgBAAAAEJRACgAAAICgBFIAAAAABCWQAgAAACAogRQAAAAAQQmkAAAAAAhKIAUAAABAUAIpAAAAAIISSAEAAAAQlEAKAAAAgKAEUgAAAAAEJZACAAAAICiBFAAAAABBCaQAAAAACEogBQAAAEBQAikAAAAAghJIAQAAABCUQAoAAACAoARSAAAAAAQlkAIAAAAgKIEUAAAAAEEJpAAAAAAISiAFAAAAQFACKQAAAACCEkgBAAAAEJRACgAAAICgBFIAAAAABCWQAgAAACAogRQAAAAAQQmkAAAAAAhKIAUAAABAUAIpAAAAAIISSAEAAAAQlEAKAAAAgKAEUgAAAAAEJZACAAAAICiBFAAAAABBCaQAAAAACEogBQAAAEBQAikAAAAAghJIAQAAABCUQAoAAACAoARSAAAAAAQlkAIAAAAgKIEUAAAAAEEJpAAAAAAISiAFAAAAQFACKQAAAACCEkgBAAAAEJRACgAAAICgBFIAAAAABCWQAgAAACAogRQAAAAAQQmkAAAAAAhKIAUAAABAUAIpAAAAAIISSAEAAAAQlEAKAAAAgKAEUgAAAAAEJZACAAAAICiBFAAAAABBCaQAAAAACEogBQAAAEBQAikAAAAAghJIAQAAABCUQAoAAACAoARSAAAAAAQlkAIAAAAgKIEUAAAAAEEJpAAAAAAISiAFAAAAQFACKQAAAACCEkgBAAAAEJRACgAAAICgBFIAAAAABCWQAgAAACAogRQAAAAAQQmkAAAAAAhKIAUAAABAUAIpAAAAAIISSAEAAAAQlEAKAAAAgKAEUgAAAAAEJZACAAAAICiBFAAAAABBCaQAAAAACEogBQAAAEBQAikAAAAAghJIAQAAABCUQAoAAACAoARSAAAAAAQlkAIAAAAgKIEUAAAAAEEJpAAAAAAISiAFAAAAQFACKQAAAACCEkgBAAAAEJRACgAAAICgBFIAAAAABCWQAgAAACAogRQAAAAAQQmkAAAAAAhKIAUAAABAUAIpAAAAAIISSAEAAAAQlEAKAAAAgKAEUgAAAAAEJZACAAAAICiBFAAAAABBCaQAAAAACEogBQAAAEBQAikAAAAAghJIAQAAABCUQAoAAACAoARSAAAAAAQlkAIAAAAgKIEUAAAAAEEJpAAAAAAISiAFAAAAQFACKQAAAACCEkgBAAAAEJRACgAAAICgBFIAAAAABCWQAgAAACAogRQAAAAAQQmkAAAAAAhKIAUAAABAUAIpAAAAAIISSAEAAAAQlEAKAAAAgKAEUgAAAAAEJZACAAAAICiBFAAAAABBCaQAAAAACEogBQAAAEBQAikAAAAAghJIAQAAABCUQAoAAACAoARSAAAAAAQlkAIAAAAgKIEUAAAAAEEJpAAAAAAISiAFAAAAQFACKQAAAACCEkgBAAAAEJRACgAAAICgBFIAAAAABCWQAgAAACAogRQAAAAAQQmkAAAAAAhKIAUAAABAUAIpAAAAAIISSAEAAAAQlEAKAAAAgKAEUgAAAAAEJZACAAAAICiBFAAAAABBCaQAAAAACEogBQAAAEBQAikAAAAAghJIAQAAABCUQAoAAACAoARSAAAAAAQlkAIAAAAgKIEUAAAAAEEJpAAAAAAISiAFAAAAQFACKQAAAACCEkgBAAAAEJRACgAAAICgBFIAAAAABCWQAgAAACAogRQAAAAAQQmkAAAAAAhKIAUAAABAUAIpAAAAAIISSAEAAAAQlEAKAAAAgKAEUgAAAAAEJZACAAAAICiBFAAAAABBCaQAAAAACEogBQAAAEBQAikAAAAAghJIAQAAABCUQAoAAACAoARSAAAAAAQlkAIAAAAgKIEUAAAAAEEJpAAAAAAISiAFAAAAQFACKQAAAACCEkgBAAAAEJRACgAAAICgBFIAAAAABCWQAgAAACAogRQAAAAAQQmkAAAAAAhKIAUAAABAUAIpAAAAAIL6/wE5NfU2KOzWzAAAAABJRU5ErkJggg==", "refs": null, "images": [[25.559999465942383, 95.51995849609375, 766.0800170898438, 205.68002319335938], [49.44000244140625, 31.44000244140625, 628.5599975585938, 95.1600341796875], [24.720001220703125, 30.719970703125, 766.3200073242188, 205.92001342773438], [25.440000534057617, 30.719970703125, 766.3200073242188, 205.92001342773438]]}]}
\ No newline at end of file
diff --git a/pdftext/__init__.py b/pdftext/__init__.py
new file mode 100644
index 0000000..879cd59
--- /dev/null
+++ b/pdftext/__init__.py
@@ -0,0 +1 @@
+"""pdftext package.""" 
\ No newline at end of file
diff --git a/pdftext/extraction.py b/pdftext/extraction.py
index 9c4e01d..bea37e3 100644
--- a/pdftext/extraction.py
+++ b/pdftext/extraction.py
@@ -3,37 +3,49 @@
 from concurrent.futures import ProcessPoolExecutor
 from functools import partial
 from itertools import repeat
-from typing import List
+from typing import List, Optional, Any, Union, cast
 
 import pypdfium2 as pdfium
 
 from pdftext.pdf.links import add_links_and_refs
 from pdftext.pdf.pages import get_pages
-from pdftext.postprocessing import handle_hyphens, merge_text, postprocess_text, sort_blocks
-from pdftext.schema import Pages, TableInputs, Tables
+from pdftext.postprocessing import (
+    handle_hyphens,
+    merge_text,
+    postprocess_text,
+    sort_blocks,
+)
+from pdftext.schema import Bbox, Pages, Span, TableInputs, Tables
 from pdftext.settings import settings
 from pdftext.tables import table_cell_text
 
+# Define global variable used in worker processes
+pdf_doc: Any = None
 
-def _load_pdf(pdf, flatten_pdf):
-    pdf = pdfium.PdfDocument(pdf)
+
+def _load_pdf(pdf: str, flatten_pdf: bool) -> Any:
+    pdf_doc = pdfium.PdfDocument(pdf)
 
     # Must be called on the parent pdf, before the page was retrieved
     if flatten_pdf:
-        pdf.init_forms()
+        pdf_doc.init_forms()
 
-    return pdf
+    return pdf_doc
 
 
-def _get_page_range(page_range, flatten_pdf=False, quote_loosebox=True) -> Pages:
+def _get_page_range(
+    page_range: List[int], flatten_pdf: bool = False, quote_loosebox: bool = True
+) -> Pages:
+    global pdf_doc
+    # Convert list to range for get_pages
     return get_pages(pdf_doc, page_range, flatten_pdf, quote_loosebox)
 
 
-def worker_shutdown(pdf_doc):
+def worker_shutdown(pdf_doc: Any) -> None:
     pdf_doc.close()
 
 
-def worker_init(pdf_path, flatten_pdf):
+def worker_init(pdf_path: str, flatten_pdf: bool) -> None:
     global pdf_doc
 
     pdf_doc = _load_pdf(pdf_path, flatten_pdf)
@@ -41,66 +53,113 @@ def worker_init(pdf_path, flatten_pdf):
     atexit.register(partial(worker_shutdown, pdf_doc))
 
 
-def _get_pages(pdf_path, page_range=None, flatten_pdf=False, quote_loosebox=True, workers=None) -> Pages:
+def _get_pages(
+    pdf_path: str,
+    page_range: Optional[List[int]] = None,
+    flatten_pdf: bool = False,
+    quote_loosebox: bool = True,
+    workers: Optional[int] = None,
+) -> Pages:
     pdf_doc = _load_pdf(pdf_path, flatten_pdf)
     if page_range is None:
-        page_range = range(len(pdf_doc))
+        page_range_obj = list(range(len(pdf_doc)))
+    else:
+        page_range_obj = page_range
 
     if workers is not None:
-        workers = min(workers, len(page_range) // settings.WORKER_PAGE_THRESHOLD)  # It's inefficient to have too many workers, since we batch in inference
+        workers = min(
+            workers, len(page_range_obj) // settings.WORKER_PAGE_THRESHOLD
+        )  # It's inefficient to have too many workers, since we batch in inference
 
     if workers is None or workers <= 1:
-        pages = get_pages(pdf_doc, page_range, flatten_pdf, quote_loosebox)
+        pages = get_pages(pdf_doc, page_range_obj, flatten_pdf, quote_loosebox)
         pdf_doc.close()
         return pages
 
     pdf_doc.close()
-    page_range = list(page_range)
-
-    pages_per_worker = math.ceil(len(page_range) / workers)
-    page_range_chunks = [page_range[i * pages_per_worker:(i + 1) * pages_per_worker] for i in range(workers)]
 
-    with ProcessPoolExecutor(max_workers=workers, initializer=worker_init, initargs=(pdf_path, flatten_pdf)) as executor:
-        pages = list(executor.map(_get_page_range, page_range_chunks, repeat(flatten_pdf), repeat(quote_loosebox)))
-
-    ordered_pages = [page for sublist in pages for page in sublist]
+    pages_per_worker = math.ceil(len(page_range_obj) / workers)
+    page_range_chunks = [
+        page_range_obj[i * pages_per_worker : (i + 1) * pages_per_worker]
+        for i in range(workers)
+    ]
+
+    with ProcessPoolExecutor(
+        max_workers=workers, initializer=worker_init, initargs=(pdf_path, flatten_pdf)
+    ) as executor:
+        pages_lists = list(
+            executor.map(
+                _get_page_range,
+                page_range_chunks,
+                repeat(flatten_pdf),
+                repeat(quote_loosebox),
+            )
+        )
+
+    ordered_pages = [page for sublist in pages_lists for page in sublist]
     return ordered_pages
 
 
-def plain_text_output(pdf_path, sort=False, hyphens=False, page_range=None, flatten_pdf=False, workers=None) -> str:
-    text = paginated_plain_text_output(pdf_path, sort=sort, hyphens=hyphens, page_range=page_range, workers=workers, flatten_pdf=flatten_pdf)
+def plain_text_output(
+    pdf_path: str,
+    sort: bool = False,
+    hyphens: bool = False,
+    page_range: Optional[List[int]] = None,
+    flatten_pdf: bool = False,
+    workers: Optional[int] = None,
+) -> str:
+    text = paginated_plain_text_output(
+        pdf_path,
+        sort=sort,
+        hyphens=hyphens,
+        page_range=page_range,
+        workers=workers,
+        flatten_pdf=flatten_pdf,
+    )
     return "\n".join(text)
 
 
-def paginated_plain_text_output(pdf_path, sort=False, hyphens=False, page_range=None, flatten_pdf=False, workers=None) -> List[str]:
-    pages: Pages = _get_pages(pdf_path, page_range, workers=workers, flatten_pdf=flatten_pdf)
+def paginated_plain_text_output(
+    pdf_path: str,
+    sort: bool = False,
+    hyphens: bool = False,
+    page_range: Optional[List[int]] = None,
+    flatten_pdf: bool = False,
+    workers: Optional[int] = None,
+) -> List[str]:
+    pages: Pages = _get_pages(
+        pdf_path, page_range, workers=workers, flatten_pdf=flatten_pdf
+    )
     text = []
     for page in pages:
         text.append(merge_text(page, sort=sort, hyphens=hyphens).strip())
     return text
 
 
-def _process_span(span, page_width, page_height, keep_chars):
-    span["bbox"] = span["bbox"].bbox
+def _process_span(
+    span: Span, page_width: int, page_height: int, keep_chars: bool
+) -> None:
     span["text"] = handle_hyphens(postprocess_text(span["text"]), keep_hyphens=True)
     if not keep_chars:
         del span["chars"]
-    else:
-        for char in span["chars"]:
-            char["bbox"] = char["bbox"].bbox
-
 
 def dictionary_output(
-        pdf_path,
-        sort=False,
-        page_range=None,
-        keep_chars=False,
-        flatten_pdf=False,
-        quote_loosebox=True,
-        disable_links=False,
-        workers=None
+    pdf_path: str,
+    sort: bool = False,
+    page_range: Optional[List[int]] = None,
+    keep_chars: bool = False,
+    flatten_pdf: bool = False,
+    quote_loosebox: bool = True,
+    disable_links: bool = False,
+    workers: Optional[int] = None,
 ) -> Pages:
-    pages: Pages = _get_pages(pdf_path, page_range, workers=workers, flatten_pdf=flatten_pdf, quote_loosebox=quote_loosebox)
+    pages: Pages = _get_pages(
+        pdf_path,
+        page_range,
+        workers=workers,
+        flatten_pdf=flatten_pdf,
+        quote_loosebox=quote_loosebox,
+    )
 
     if not disable_links:
         pdf = _load_pdf(pdf_path, False)
@@ -113,12 +172,10 @@ def dictionary_output(
             for k in list(block.keys()):
                 if k not in ["lines", "bbox"]:
                     del block[k]
-            block["bbox"] = block["bbox"].bbox
             for line in block["lines"]:
                 for k in list(line.keys()):
                     if k not in ["spans", "bbox"]:
                         del line[k]
-                line["bbox"] = line["bbox"].bbox
                 for span in line["spans"]:
                     _process_span(span, page_width, page_height, keep_chars)
 
@@ -127,29 +184,48 @@ def dictionary_output(
 
         if page["rotation"] == 90 or page["rotation"] == 270:
             page["width"], page["height"] = page["height"], page["width"]
-            page["bbox"] = [page["bbox"][2], page["bbox"][3], page["bbox"][0], page["bbox"][1]]
+
+            # Create a new Bbox instance from the list of floats
+            bbox_list = [
+                page["bbox"][2],
+                page["bbox"][3],
+                page["bbox"][0],
+                page["bbox"][1],
+            ]
+            page["bbox"] = Bbox(bbox_list)
     return pages
 
 
 def table_output(
     pdf_path: str,
     table_inputs: TableInputs,
-    page_range=None,
-    flatten_pdf=False,
-    quote_loosebox=True,
-    workers=None,
-    pages: Pages | None = None
+    page_range: Optional[List[int]] = None,
+    flatten_pdf: bool = False,
+    quote_loosebox: bool = True,
+    workers: Optional[int] = None,
+    pages: Optional[Pages] = None,
 ) -> List[Tables]:
     # Extract pages if they don't exist
-    if not pages:
-        pages: Pages = dictionary_output(pdf_path, page_range=page_range, flatten_pdf=flatten_pdf, quote_loosebox=quote_loosebox, workers=workers, keep_chars=True)
-
-    assert len(pages) == len(table_inputs), "Number of pages and table inputs must match"
+    if pages is None:
+        pages = dictionary_output(
+            pdf_path,
+            page_range=page_range,
+            flatten_pdf=flatten_pdf,
+            quote_loosebox=quote_loosebox,
+            workers=workers,
+            keep_chars=True,
+        )
+
+    assert len(pages) == len(
+        table_inputs
+    ), "Number of pages and table inputs must match"
 
     # Extract table cells per page
     out_tables = []
     for page, table_input in zip(pages, table_inputs):
         tables = table_cell_text(table_input["tables"], page, table_input["img_size"])
-        assert len(tables) == len(table_input["tables"]), "Number of tables and table inputs must match"
+        assert len(tables) == len(
+            table_input["tables"]
+        ), "Number of tables and table inputs must match"
         out_tables.append(tables)
     return out_tables
diff --git a/pdftext/pdf/chars.py b/pdftext/pdf/chars.py
index 2a06a1d..5011913 100644
--- a/pdftext/pdf/chars.py
+++ b/pdftext/pdf/chars.py
@@ -1,13 +1,20 @@
+import ctypes
 import math
+from typing import Optional
 
 import pypdfium2 as pdfium
 import pypdfium2.raw as pdfium_c
+from pypdfium2.raw import c_uint
 
-from pdftext.pdf.utils import get_fontname
+from pdftext.pdf.utils import get_fontname, transform_bbox
 from pdftext.schema import Bbox, Char, Chars, Spans, Span
 
-
-def get_chars(textpage: pdfium.PdfTextPage, page_bbox: list[float], page_rotation: int, quote_loosebox=True) -> Chars:
+def get_chars(
+    textpage: pdfium.PdfTextPage,
+    page_bbox: list[float],
+    page_rotation: int,
+    quote_loosebox: bool = True,
+) -> Chars:
     chars: Chars = []
 
     x_start, y_start, x_end, y_end = page_bbox
@@ -21,22 +28,27 @@ def get_chars(textpage: pdfium.PdfTextPage, page_bbox: list[float], page_rotatio
         loosebox = (rotation == 0) and (text != "'" or quote_loosebox)
 
         char_box = textpage.get_charbox(i, loose=loosebox)
-        cx_start, cy_start, cx_end, cy_end = char_box
-
-        cx_start -= x_start
-        cx_end -= x_start
-        cy_start -= y_start
-        cy_end -= y_start
 
-        ty_start = page_height - cy_start
-        ty_end = page_height - cy_end
-
-        bbox_coords = [min(cx_start, cx_end), min(ty_start, ty_end), max(cx_start, cx_end), max(ty_start, ty_end)]
-        bbox = Bbox(bbox_coords).rotate(page_width, page_height, page_rotation)
+        bbox = transform_bbox(page_bbox, page_rotation, char_box)
 
         fontname, fontflag = get_fontname(textpage, i)
         fontsize = pdfium_c.FPDFText_GetFontSize(textpage, i)
         fontweight = pdfium_c.FPDFText_GetFontWeight(textpage, i)
+        fontcolor = [c_uint()]*4  # r, g, b, a
+        is_fillcolor = round(
+            pdfium_c.FPDFText_GetFillColor(
+                textpage,
+                i,
+                ctypes.byref(fontcolor[0]),
+                ctypes.byref(fontcolor[1]),
+                ctypes.byref(fontcolor[2]),
+                ctypes.byref(fontcolor[3]),
+            )
+        )
+        if is_fillcolor:
+            fontcolor = [color.value for color in fontcolor]
+        else:
+            fontcolor = []
 
         char_dict: Char = {
             "bbox": bbox,
@@ -45,32 +57,39 @@ def get_chars(textpage: pdfium.PdfTextPage, page_bbox: list[float], page_rotatio
             "font": {
                 "name": fontname,
                 "flags": fontflag,
+                "color": fontcolor,
                 "size": fontsize,
                 "weight": fontweight,
             },
-            "char_idx": i
+            "char_idx": i,
         }
         chars.append(char_dict)
 
+    # TODO: If required, add a deduplication step here through intersection of bboxes
+
     return chars
 
 
 def deduplicate_chars(chars: Chars) -> Chars:
     # we first construct words from the chars and then deduplicate them
     words: Spans = []
-    word: Span = None
-
-    def word_break():
-        words.append({
-            "bbox": char["bbox"],
-            "text": char["char"],
-            "rotation": char["rotation"],
-            "font": char["font"],
-            "char_start_idx": char["char_idx"],
-            "char_end_idx": char["char_idx"],
-            "chars": [char],
-            "url": '',
-        })
+    word: Optional[Span] = None
+
+    def word_break() -> None:
+        words.append(
+            {
+                "bbox": char["bbox"],
+                "text": char["char"],
+                "rotation": int(char["rotation"]),
+                "font": char["font"],
+                "char_start_idx": char["char_idx"],
+                "char_end_idx": char["char_idx"],
+                "chars": [char],
+                "url": "",
+                "superscript": False,
+                "subscript": False,
+            }
+        )
 
     for char in chars:
         if words:
@@ -81,7 +100,7 @@ def word_break():
             continue
 
         # we also break on hyphenation
-        if any(word['text'].endswith(x) for x in ['\n', ' ', '\x02']):
+        if any(word["text"].endswith(x) for x in ["\n", " ", "\x02"]):
             word_break()
             continue
 
@@ -92,14 +111,14 @@ def word_break():
             word_break()
             continue
 
-        if char['rotation'] != word['rotation']:
+        if char["rotation"] != word["rotation"]:
             word_break()
             continue
 
-        word['text'] += char['char']
-        word['char_end_idx'] = char['char_idx']
-        word['bbox'] = word['bbox'].merge(char['bbox'])
-        word['chars'].append(char)
+        word["text"] += char["char"]
+        word["char_end_idx"] = char["char_idx"]
+        word["bbox"] = word["bbox"].merge(char["bbox"])
+        word["chars"].append(char)
 
     # deduplicate words - use tuple keys instead of strings
     seen = set()
@@ -116,4 +135,4 @@ def word_break():
             seen.add(key)
             deduped.append(word)
 
-    return [char for word in deduped for char in word['chars']]
+    return [char for word in deduped for char in word["chars"]]
diff --git a/pdftext/pdf/links.py b/pdftext/pdf/links.py
index c8f5232..6b0d2a1 100644
--- a/pdftext/pdf/links.py
+++ b/pdftext/pdf/links.py
@@ -1,6 +1,6 @@
 import ctypes
 import math
-from typing import Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 import pypdfium2 as pdfium
 import pypdfium2.raw as pdfium_c
@@ -9,7 +9,7 @@
 from pdftext.schema import Bbox, Link, Page, PageReference, Pages, Span
 
 
-def _get_dest_position(dest) -> Optional[Tuple[float, float]]:
+def _get_dest_position(dest: Any) -> Optional[Tuple[float, float]]:
     has_x = ctypes.c_int()
     has_y = ctypes.c_int()
     has_zoom = ctypes.c_int()
@@ -26,7 +26,7 @@ def _get_dest_position(dest) -> Optional[Tuple[float, float]]:
     return None
 
 
-def _rect_to_scaled_bbox(rect, page_bbox, page_rotation) -> List[float]:
+def _rect_to_scaled_bbox(rect: List[float], page_bbox: List[float], page_rotation: int) -> List[float]:
     page_width = math.ceil(abs(page_bbox[2] - page_bbox[0]))
     page_height = math.ceil(abs(page_bbox[1] - page_bbox[3]))
 
@@ -44,7 +44,7 @@ def _rect_to_scaled_bbox(rect, page_bbox, page_rotation) -> List[float]:
     return Bbox(bbox).rotate(page_width, page_height, page_rotation).bbox
 
 
-def _xy_to_scaled_pos(x, y, page_bbox, page_rotation, expand_by=1) -> List[float]:
+def _xy_to_scaled_pos(x: float, y: float, page_bbox: List[float], page_rotation: int, expand_by: float = 1.0) -> List[float]:
     return _rect_to_scaled_bbox([x - expand_by, y - expand_by, x + expand_by, y + expand_by], page_bbox, page_rotation)[:2]
 
 
@@ -61,13 +61,6 @@ def get_links(page_idx: int, pdf: pdfium.PdfDocument) -> List[Link]:
 
     annot_count = pdfium_c.FPDFPage_GetAnnotCount(page)
     for i in range(annot_count):
-        link: Link = {
-            'page': page_idx,
-            'bbox': None,
-            'dest_page': None,
-            'dest_pos': None,
-            'url': None,
-        }
         annot = pdfium_c.FPDFPage_GetAnnot(page, i)
         if pdfium_c.FPDFAnnot_GetSubtype(annot) != pdfium_c.FPDF_ANNOT_LINK:
             continue
@@ -77,11 +70,19 @@ def get_links(page_idx: int, pdf: pdfium.PdfDocument) -> List[Link]:
         if not success:
             continue
 
-        link['bbox'] = _rect_to_scaled_bbox(
+        link_bbox = _rect_to_scaled_bbox(
             [fs_rect.left, fs_rect.top, fs_rect.right, fs_rect.bottom],
             page_bbox, page_rotation
         )
 
+        link: Link = {
+            'page': page_idx,
+            'bbox': link_bbox,
+            'dest_page': None,
+            'dest_pos': None,
+            'url': None,
+        }
+
         link_obj = pdfium_c.FPDFAnnot_GetLink(annot)
 
         dest = pdfium_c.FPDFLink_GetDest(pdf, link_obj)
@@ -122,7 +123,7 @@ def get_links(page_idx: int, pdf: pdfium.PdfDocument) -> List[Link]:
     return urls
 
 
-def merge_links(page: Page, pdf: pdfium.PdfDocument, refs: PageReference):
+def merge_links(page: Page, pdf: pdfium.PdfDocument, refs: PageReference) -> None:
     """
     Merges links with spans. Some spans can also have multiple links associated with them.
     We break up the spans and reconstruct them taking the links into account.
@@ -176,12 +177,12 @@ def merge_links(page: Page, pdf: pdfium.PdfDocument, refs: PageReference):
             line['spans'] = spans
 
 
-def _reconstruct_spans(orig_span: dict, links: List[Link]) -> List[Span]:
+def _reconstruct_spans(orig_span: Span, links: List[Link]) -> List[Span]:
     """
     Reconstructs the spans by breaking them up into smaller spans based on the links.
     """
     spans: List[Span] = []
-    span: Span = None
+    span: Optional[Span] = None
     link_bboxes = [Bbox(link['bbox']) for link in links]
 
     for char in orig_span['chars']:
@@ -195,21 +196,23 @@ def _reconstruct_spans(orig_span: dict, links: List[Link]) -> List[Span]:
             if area > 0:
                 intersections.append((area, links[i]))
 
-        current_url = ''
+        current_url: Optional[str] = None
         if intersections:
             intersections.sort(key=lambda x: x[0], reverse=True)
             current_url = intersections[0][1]['url']
 
-        if not span or current_url != span['url']:
+        if not span or (current_url or '') != span['url']:
             span = {
                 "bbox": char_bbox,
                 "text": char["char"],
-                "rotation": char["rotation"],
+                "rotation": int(char["rotation"]),
                 "font": char["font"],
                 "char_start_idx": char["char_idx"],
                 "char_end_idx": char["char_idx"],
                 "chars": [char],
-                "url": current_url,
+                "url": current_url or '',
+                "superscript": False,
+                "subscript": False,
             }
             spans.append(span)
         else:
@@ -221,7 +224,7 @@ def _reconstruct_spans(orig_span: dict, links: List[Link]) -> List[Span]:
     return spans
 
 
-def add_links_and_refs(pages: Pages, pdf_doc: pdfium.PdfDocument):
+def add_links_and_refs(pages: Pages, pdf_doc: pdfium.PdfDocument) -> None:
     refs = PageReference()
 
     for page in pages:
diff --git a/pdftext/pdf/pages.py b/pdftext/pdf/pages.py
index c478df1..4d4bb46 100644
--- a/pdftext/pdf/pages.py
+++ b/pdftext/pdf/pages.py
@@ -2,26 +2,46 @@
 
 import math
 import statistics
-from typing import List
+from typing import Optional, cast
 import unicodedata
+import io
 
 import pypdfium2 as pdfium
 
 from pdftext.pdf.chars import get_chars, deduplicate_chars
-from pdftext.pdf.utils import flatten
-from pdftext.schema import Blocks, Chars, Line, Lines, Pages, Span, Spans
-
-
-def is_math_symbol(char):
+from pdftext.pdf.utils import (
+    Bbox,
+    flatten,
+    transform_bbox,
+    remove_wrong_bboxes,
+)
+from pdftext.schema import (
+    Blocks,
+    Chars,
+    Line,
+    Lines,
+    Pages,
+    Span,
+    Spans,
+    Block,
+    Page,
+)
+import base64
+
+
+def is_math_symbol(char: str) -> bool:
     if len(char) != 1:
         return False
 
     category = unicodedata.category(char)
-    return category == 'Sm'
+    return category == "Sm"
 
-def assign_scripts(lines: Lines, height_threshold: float = 0.8, line_distance_threshold: float = 0.1):
+
+def assign_scripts(
+    lines: Lines, height_threshold: float = 0.8, line_distance_threshold: float = 0.1
+) -> None:
     for line in lines:
-        prev_span = None
+        prev_span: Optional[Span] = None
         if len(line["spans"]) < 2:
             continue
 
@@ -30,56 +50,104 @@ def assign_scripts(lines: Lines, height_threshold: float = 0.8, line_distance_th
             continue
 
         for i, span in enumerate(line["spans"]):
-            is_first = i == 0 or not prev_span["text"].strip()
-            is_last = i == len(line["spans"]) - 1 or not line["spans"][i + 1]["text"].strip()
+            is_first = i == 0 or not (prev_span and prev_span["text"].strip())
+            is_last = (
+                i == len(line["spans"]) - 1 or not line["spans"][i + 1]["text"].strip()
+            )
             span_height = span["bbox"].height
             span_top = span["bbox"].y_start
             span_bottom = span["bbox"].y_end
 
-            line_fullheight = span_height / max(1, line["bbox"].height) <= height_threshold
-            next_fullheight = is_last or span_height / max(1, line["spans"][i + 1]["bbox"].height) <= height_threshold
-            prev_fullheight = is_first or span_height / max(1, prev_span["bbox"].height) <= height_threshold
-
-            above = any([span_top < (s["bbox"].y_start - s["bbox"].height * line_distance_threshold) for j, s in enumerate(line["spans"]) if j != i])
-            prev_above = is_first or span_top < prev_span["bbox"].y_start
+            line_fullheight = (
+                span_height / max(1, line["bbox"].height) <= height_threshold
+            )
+            next_fullheight = (
+                is_last
+                or span_height / max(1, line["spans"][i + 1]["bbox"].height)
+                <= height_threshold
+            )
+            prev_fullheight = (
+                is_first
+                or not prev_span
+                or (span_height / max(1, prev_span["bbox"].height) <= height_threshold)
+            )
+
+            above = any(
+                [
+                    span_top
+                    < (s["bbox"].y_start - s["bbox"].height * line_distance_threshold)
+                    for j, s in enumerate(line["spans"])
+                    if j != i
+                ]
+            )
+            prev_above = (
+                is_first or not prev_span or span_top < prev_span["bbox"].y_start
+            )
             next_above = is_last or span_top < line["spans"][i + 1]["bbox"].y_start
 
-            below = any([span_bottom > (s["bbox"].y_end + s["bbox"].height * line_distance_threshold) for j, s in enumerate(line["spans"]) if j != i])
-            prev_below = is_first or span_bottom > prev_span["bbox"].y_end
+            below = any(
+                [
+                    span_bottom
+                    > (s["bbox"].y_end + s["bbox"].height * line_distance_threshold)
+                    for j, s in enumerate(line["spans"])
+                    if j != i
+                ]
+            )
+            prev_below = (
+                is_first or not prev_span or span_bottom > prev_span["bbox"].y_end
+            )
             next_below = is_last or span_bottom > line["spans"][i + 1]["bbox"].y_end
 
             span_text = span["text"].strip()
-            span_text_okay = all([
-                (len(span_text) == 1 or span_text.isdigit()), # Ensure that the span text is a single char or a number
-                span_text.isalnum() or is_math_symbol(span_text) # Ensure that the span text is an alphanumeric or a math symbol
-            ])
-
-            if all([
-                (prev_fullheight or next_fullheight),
-                (prev_above or next_above),
-                above,
-                line_fullheight,
-                span_text_okay
-            ]):
+            span_text_okay = all(
+                [
+                    (
+                        len(span_text) == 1 or span_text.isdigit()
+                    ),  # Ensure that the span text is a single char or a number
+                    span_text.isalnum()
+                    or is_math_symbol(
+                        span_text
+                    ),  # Ensure that the span text is an alphanumeric or a math symbol
+                ]
+            )
+
+            if all(
+                [
+                    (prev_fullheight or next_fullheight),
+                    (prev_above or next_above),
+                    above,
+                    line_fullheight,
+                    span_text_okay,
+                ]
+            ):
                 span["superscript"] = True
-            elif all([
-                (prev_fullheight or next_fullheight),
-                (prev_below or next_below),
-                below,
-                line_fullheight,
-                span_text_okay
-            ]):
+            elif all(
+                [
+                    (prev_fullheight or next_fullheight),
+                    (prev_below or next_below),
+                    below,
+                    line_fullheight,
+                    span_text_okay,
+                ]
+            ):
                 span["subscript"] = True
 
             prev_span = span
 
 
-def get_spans(chars: Chars, superscript_height_threshold: float = 0.8, line_distance_threshold: float = 0.1) -> Spans:
+def get_spans(
+    chars: Chars,
+    image_bboxes: list[Bbox],
+    superscript_height_threshold: float = 0.8,
+    line_distance_threshold: float = 0.1,
+) -> Spans:
     spans: Spans = []
-    span: Span = None
+    avg_char_width: Optional[float] = None
+    sum_char_widths = 0.0
 
-    def span_break():
-        spans.append({
+    def span_break() -> None:
+        nonlocal spans, sum_char_widths
+        new_span_dict = {
             "bbox": char["bbox"],
             "text": char["char"],
             "rotation": char["rotation"],
@@ -87,80 +155,131 @@ def span_break():
             "char_start_idx": char["char_idx"],
             "char_end_idx": char["char_idx"],
             "chars": [char],
-            "url": '',
-        })
+            "url": "",
+            "superscript": False,
+            "subscript": False,
+        }
+        spans.append(Span(new_span_dict))
+        sum_char_widths = char["bbox"].width
 
     for char in chars:
-        if spans:
-            span = spans[-1]
+        current_span = spans[-1] if spans else None
 
-        if not span:
+        if not current_span:
             span_break()
             continue
 
-        # we break on any change in font info
-        if any(char['font'][k] != span['font'][k] for k in ['name', 'flags', 'size', 'weight']):
+        if any(
+            char["font"][k] != current_span["font"][k]
+            for k in ["name", "flags", "size", "weight", "color"]
+        ):
             span_break()
             continue
 
-        if char['rotation'] != span['rotation']:
+        if char["rotation"] != current_span["rotation"]:
             span_break()
             continue
 
-        # we break on hyphenation or newline
-        if span['text'].endswith("\x02") or span['text'].endswith("\n"):
+        if current_span["text"].endswith("\x02") or current_span["text"].endswith("\n"):
             span_break()
             continue
 
-        # Character is likely a superscript
-        if all([
-            char["bbox"][1] < (span["bbox"][1] - span["bbox"].height * line_distance_threshold), # char top is above span
-            char["bbox"][3] < (span["bbox"].height * superscript_height_threshold) + span["bbox"][1], # char bottom is not full line height
-            char["bbox"][0] > span["bbox"][2], # char is to the right of the span
-        ]):
+        if all(
+            [
+                char["bbox"].y_start
+                < (
+                    current_span["bbox"].y_start
+                    - current_span["bbox"].height * line_distance_threshold
+                ),
+                char["bbox"].y_end
+                < (current_span["bbox"].height * superscript_height_threshold)
+                + current_span["bbox"].y_start,
+                char["bbox"].x_start > current_span["bbox"].x_end,
+            ]
+        ):
             span_break()
             continue
 
-        span['text'] += char['char']
-        span['char_end_idx'] = char['char_idx']
-        span['bbox'] = span['bbox'].merge(char['bbox'])
-        span['chars'].append(char)
-
-    return spans
+        if len(current_span["chars"]) > 0:
+            prev_char_bbox = current_span["chars"][-1]["bbox"]
+            for char_in_current_span in current_span["chars"][::-1]:
+                if char_in_current_span["char"].strip():
+                    prev_char_bbox = char_in_current_span["bbox"]
+                    break
+            avg_char_width = sum_char_widths / len(current_span["chars"])
+            # Used to be 1.5 * avg_char_width reduced it to avg_char_width
+            if char["bbox"].horizontal_distance(prev_char_bbox) > avg_char_width:
+                span_break()
+                continue
+
+            if char["bbox"].overlap_y(prev_char_bbox) == 0:
+                span_break()
+                continue
+
+            if char["bbox"].overlap_y(current_span["bbox"]) < 0.1 * min(
+                char["bbox"].height, current_span["bbox"].height
+            ):
+                span_break()
+                continue
+
+            for image_bbox in image_bboxes:
+                if (
+                    image_bbox.intersection_area(current_span["bbox"]) == 0
+                    and image_bbox.intersection_area(char["bbox"]) == 0
+                    and image_bbox.intersection_area(
+                        current_span["bbox"].merge(char["bbox"])
+                    )
+                    > 0
+                ):
+                    span_break()
+                    continue
+
+        current_span["text"] += char["char"]
+        current_span["char_end_idx"] = char["char_idx"]
+        current_span["bbox"] = current_span["bbox"].merge(char["bbox"])
+        current_span["chars"].append(char)
+        sum_char_widths += char["bbox"].width
+
+    filtered_spans = [span for span in spans if span["text"].strip() != ""]
+    return filtered_spans
 
 
 def get_lines(spans: Spans) -> Lines:
     lines: Lines = []
-    line: Line = None
+    current_line: Optional[Line] = None
 
-    def line_break():
-        lines.append({"spans": [span], "bbox": span["bbox"], "rotation": span["rotation"]})
+    def line_break() -> None:
+        nonlocal lines
+        lines.append(
+            {"spans": [span], "bbox": span["bbox"], "rotation": span["rotation"]}
+        )
 
     for span in spans:
-        if lines:
-            line = lines[-1]
+        current_line = lines[-1] if lines else None
 
-        if not line:
+        if not current_line:
             line_break()
             continue
 
         # we break if the previous span ends with a linebreak
-        last_text = line["spans"][-1]["text"]
+        last_text = current_line["spans"][-1]["text"]
         if any(last_text.endswith(suffix) for suffix in ["\n", "\x02"]):
             line_break()
             continue
 
-        if span["rotation"] != line["rotation"] and abs(span["rotation"] - line["rotation"]) >= 45:
+        if (
+            span["rotation"] != current_line["rotation"]
+            and abs(span["rotation"] - current_line["rotation"]) >= 45
+        ):
             line_break()
             continue
 
-        # sometimes pdfium doesn't inject a linebreak, so we check the span positions
-        if span["bbox"].y_start > line["bbox"].y_end:
+        if span["bbox"].y_start > current_line["bbox"].y_end:
             line_break()
             continue
 
-        line["spans"].append(span)
-        line["bbox"] = line["bbox"].merge(span["bbox"])
+        current_line["spans"].append(span)
+        current_line["bbox"] = current_line["bbox"].merge(span["bbox"])
 
     return lines
 
@@ -169,41 +288,45 @@ def get_blocks(lines: Lines) -> Blocks:
     if not lines:
         return []
 
-    x_diffs = []
-    y_diffs = []
+    x_diffs: list[float] = []
+    y_diffs: list[float] = []
     for i in range(len(lines) - 1):
-        prev_center = lines[i]["bbox"].center
-        curr_center = lines[i + 1]["bbox"].center
+        prev_bbox = lines[i]["bbox"]
+        curr_bbox = lines[i + 1]["bbox"]
+        prev_center = prev_bbox.center
+        curr_center = curr_bbox.center
         x_diffs.append(abs(curr_center[0] - prev_center[0]))
         y_diffs.append(abs(curr_center[1] - prev_center[1]))
 
-    median_x_gap = 0.1
-    if x_diffs:
-        median_x_gap = statistics.median(x_diffs) or median_x_gap
-    median_y_gap = 0.1
-    if y_diffs:
-        median_y_gap = statistics.median(y_diffs) or median_y_gap
+    median_x_gap = statistics.median(x_diffs) if x_diffs else 0.1
+    median_y_gap = statistics.median(y_diffs) if y_diffs else 0.1
 
     tolerance_factor = 1.5
     allowed_x_gap = median_x_gap * tolerance_factor
     allowed_y_gap = median_y_gap * tolerance_factor
 
-    def block_merge():
+    def block_merge() -> None:
+        nonlocal current_block, line
+        block: Block = cast(Block, current_block)
         block["lines"].append(line)
         block["bbox"] = block["bbox"].merge(line["bbox"])
 
     blocks: Blocks = []
     for line in lines:
-        if not blocks:
-            # First block
-            blocks.append({"lines": [line], "bbox": line["bbox"], "rotation": line["rotation"]})
+        current_block = blocks[-1] if blocks else None
+
+        if not current_block:
+            blocks.append(
+                {"lines": [line], "bbox": line["bbox"], "rotation": line["rotation"]}
+            )
             continue
 
-        block = blocks[-1]
-        last_line = block["lines"][-1]
+        last_line = cast(Block, current_block)["lines"][-1]
+        last_bbox = last_line["bbox"]
+        current_bbox = line["bbox"]
 
-        last_center = last_line["bbox"].center
-        current_center = line["bbox"].center
+        last_center = last_bbox.center
+        current_center = current_bbox.center
 
         x_diff = abs(current_center[0] - last_center[0])
         y_diff = abs(current_center[1] - last_center[1])
@@ -215,7 +338,11 @@ def block_merge():
 
         # we make an exception for the first line w.r.t the x diff, because the first line is usually indented
         line_x_indented_start = last_line["bbox"].x_start > line["bbox"].x_start
-        if len(block["lines"]) == 1 and line_x_indented_start and y_diff <= allowed_y_gap:
+        if (
+            len(cast(Block, current_block)["lines"]) == 1
+            and line_x_indented_start
+            and y_diff <= allowed_y_gap
+        ):
             block_merge()
             continue
 
@@ -226,31 +353,35 @@ def block_merge():
             continue
 
         # if the y diff is very small, and you see a line continuation, we merge (can happen with inline math between text spans)
-        if y_diff < allowed_y_gap * 0.2 and last_line["bbox"].x_end > line["bbox"].x_start:
+        if (
+            y_diff < allowed_y_gap * 0.2
+            and last_line["bbox"].x_end > line["bbox"].x_start
+        ):
             block_merge()
             continue
 
-        # we also merge when we see the current line intersecting the previous block
-        if block["bbox"].intersection_pct(line["bbox"]) > 0:
+        if cast(Block, current_block)["bbox"].intersection_pct(line["bbox"]) > 0:
             block_merge()
             continue
 
-        blocks.append({"lines": [line], "bbox": line["bbox"]})
+        blocks.append(
+            {"lines": [line], "bbox": line["bbox"], "rotation": line["rotation"]}
+        )
 
-    # we do one last pass of merging overlapping blocks in the PDF reading order
-    merged_blocks = []
-    for i in range(len(blocks)):
-        if not merged_blocks:
-            merged_blocks.append(blocks[i])
-            continue
+    merged_blocks: Blocks = []
+    if not blocks:
+        return []
 
+    merged_blocks.append(blocks[0])
+    for i in range(1, len(blocks)):
         prev_block = merged_blocks[-1]
         curr_block = blocks[i]
 
         if prev_block["bbox"].intersection_pct(curr_block["bbox"]) > 0:
             merged_blocks[-1] = {
                 "lines": prev_block["lines"] + curr_block["lines"],
-                "bbox": prev_block["bbox"].merge(curr_block["bbox"])
+                "bbox": prev_block["bbox"].merge(curr_block["bbox"]),
+                "rotation": prev_block["rotation"],
             }
         else:
             merged_blocks.append(curr_block)
@@ -258,46 +389,131 @@ def block_merge():
     return merged_blocks
 
 
+def get_image_bboxes(
+    page: pdfium.PdfPage, page_bbox_list: list[float], page_rotation: int
+) -> list[Bbox]:
+
+    objects = list(page.get_objects())
+
+    text_bboxes: list[Bbox] = []
+    non_text_objects = []
+
+    for obj in objects:
+        if obj.type in (0, 5):
+            continue
+
+        if obj.type in (2, 3, 4):
+            non_text_objects.append(obj)
+            continue
+
+        text_bboxes.append(transform_bbox(page_bbox_list, page_rotation, obj.get_pos()))
+
+    for obj in objects:
+        if obj.type not in (0, 5):
+            continue
+
+        obj_bbox = transform_bbox(page_bbox_list, page_rotation, obj.get_pos())
+        overlaps_with_text = any(
+            text_bbox and obj_bbox.intersection_area(text_bbox) > 0
+            for text_bbox in text_bboxes
+        )
+
+        if not overlaps_with_text:
+            non_text_objects.append(obj)
+            continue
+
+    non_text_bboxes: list[Bbox] = [
+        transform_bbox(page_bbox_list, page_rotation, obj.get_pos())
+        for obj in non_text_objects
+    ]
+
+    filtered_non_text_bboxes: list[Optional[Bbox]] = remove_wrong_bboxes(
+        non_text_bboxes, page_bbox_list, page
+    )
+    valid_non_text_bboxes: list[Bbox] = [
+        bbox for bbox in filtered_non_text_bboxes if bbox is not None
+    ]
+
+    return valid_non_text_bboxes
+
+
 def get_pages(
     pdf: pdfium.PdfDocument,
-    page_range: range,
+    page_range: list[int],
     flatten_pdf: bool = True,
-    quote_loosebox: bool =True,
+    quote_loosebox: bool = True,
     superscript_height_threshold: float = 0.7,
     line_distance_threshold: float = 0.1,
+    page_scale: int = 2,
 ) -> Pages:
     pages: Pages = []
 
     for page_idx in page_range:
-        page = pdf.get_page(page_idx)
+        page_obj = pdf.get_page(page_idx)
         if flatten_pdf:
-            flatten(page)
-            page = pdf.get_page(page_idx)
+            flatten(page_obj)
+            page_obj = pdf.get_page(page_idx)
 
-        textpage = page.get_textpage()
+        textpage = page_obj.get_textpage()
 
-        page_bbox: List[float] = page.get_bbox()
-        page_width = math.ceil(abs(page_bbox[2] - page_bbox[0]))
-        page_height = math.ceil(abs(page_bbox[1] - page_bbox[3]))
+        page_bbox_list: list[float] = page_obj.get_bbox()
+        page_width = math.ceil(abs(page_bbox_list[2] - page_bbox_list[0]))
+        page_height = math.ceil(abs(page_bbox_list[1] - page_bbox_list[3]))
+
+        page_bbox: Bbox = Bbox(bbox=page_bbox_list)
 
         page_rotation = 0
         try:
-            page_rotation = page.get_rotation()
-        except:
+            page_rotation = page_obj.get_rotation()
+        except Exception:
             pass
 
-        chars = deduplicate_chars(get_chars(textpage, page_bbox, page_rotation, quote_loosebox))
-        spans = get_spans(chars, superscript_height_threshold=superscript_height_threshold, line_distance_threshold=line_distance_threshold)
+        image_bboxes = get_image_bboxes(page_obj, page_bbox_list, page_rotation)
+
+        chars = deduplicate_chars(
+            get_chars(textpage, page_bbox_list, page_rotation, quote_loosebox)
+        )
+        spans = get_spans(
+            chars,
+            image_bboxes,
+            superscript_height_threshold=superscript_height_threshold,
+            line_distance_threshold=line_distance_threshold,
+        )
         lines = get_lines(spans)
-        assign_scripts(lines, height_threshold=superscript_height_threshold, line_distance_threshold=line_distance_threshold)
+        assign_scripts(
+            lines,
+            height_threshold=superscript_height_threshold,
+            line_distance_threshold=line_distance_threshold,
+        )
         blocks = get_blocks(lines)
+        img_render = page_obj.render(scale=page_scale)
+        pil_image = img_render.to_pil()
 
-        pages.append({
+        bytes_arr = io.BytesIO()
+        img_base64 = ""
+        try:
+            pil_image.save(bytes_arr, format="PNG")
+            bytes_arr.seek(0)
+            img_base64 = base64.b64encode(bytes_arr.getvalue()).decode("utf-8")
+        except Exception as e:
+            print(f"Error processing image for page {page_idx}: {e}")
+        finally:
+            bytes_arr.close()
+            if "pil_image" in locals() and pil_image:
+                pil_image.close()
+
+        page_data: Page = {
             "page": page_idx,
             "bbox": page_bbox,
             "width": page_width,
             "height": page_height,
             "rotation": page_rotation,
-            "blocks": blocks
-        })
+            "blocks": blocks,
+            "scale": page_scale,
+            "page_image": img_base64,
+            "refs": None,
+            "images": image_bboxes,
+        }
+        pages.append(page_data)
+
     return pages
diff --git a/pdftext/pdf/utils.py b/pdftext/pdf/utils.py
index 0412c39..36b3c00 100644
--- a/pdftext/pdf/utils.py
+++ b/pdftext/pdf/utils.py
@@ -1,9 +1,11 @@
 from ctypes import byref, c_int, create_string_buffer
-from typing import List
+import math
+from typing import List, Tuple, Optional
 
 import numpy as np
 import pypdfium2 as pdfium
 import pypdfium2.raw as pdfium_c
+from pdftext.schema import Bbox
 
 LINE_BREAKS = ["\n", "\u000D", "\u000A"]
 TABS = ["\t", "\u0009", "\x09"]
@@ -11,13 +13,122 @@
 WHITESPACE_CHARS = ["\n", "\r", "\f", "\t", " "]
 
 
-def flatten(page, flag=pdfium_c.FLAT_NORMALDISPLAY):
+def get_page_properties(
+        page_bbox: list[float],
+        page: pdfium.PdfPage,
+        rotate: bool = False,
+) -> Tuple[int, int, int, bool]:
+    
+    x_start, y_start, x_end, y_end = page_bbox
+
+    page_width = math.ceil(abs(x_end - x_start))
+    page_height = math.ceil(abs(y_end - y_start))
+
+    page_rotation = 0
+    try:
+        page_rotation = page.get_rotation()
+    except:
+        pass
+
+
+    # This is done to deliberately use in the situations where we don't want to make this transformation 
+    # Ideally everywhere we compute the page properties, we should use this function 
+    if rotate: 
+        if page_rotation == 90 or page_rotation == 270:
+            page_width, page_height = page_height, page_width
+    
+    mediabox = page.get_mediabox()
+
+    bl_origin = mediabox[0] == 0 and mediabox[1] == 0
+
+    return page_width, page_height, page_rotation, bl_origin
+
+def remove_wrong_bboxes(
+        transformed_bboxes: list[Bbox],
+        page_bbox: list[float],
+        page: pdfium.PdfPage,
+        # page_idx: int,
+) -> List[Optional[Bbox]]:
+    
+    _, _, page_rotation, _ = get_page_properties(page_bbox, page, rotate=True)
+
+    # get_pos -> get_bbox. Hopefully correct.
+    transformed_page_bbox = transform_bbox(page_bbox, page_rotation, page.get_bbox())
+
+    correct_bboxes: List[Optional[Bbox]] = []
+    for box_objs in transformed_bboxes:
+        if box_objs: 
+            new_pos = [0.0] * 4
+            new_pos[0] = max(box_objs[0], transformed_page_bbox[0])
+            new_pos[1] = max(box_objs[1], transformed_page_bbox[1])
+            new_pos[2] = min(box_objs[2], transformed_page_bbox[2])
+            new_pos[3] = min(box_objs[3], transformed_page_bbox[3])
+
+            correct_bboxes.append(Bbox(new_pos))
+        else:
+            correct_bboxes.append(None)
+            continue 
+        
+    correct_bboxes = [
+        None if (box_objs is None or (box_objs[0] > box_objs[2]) or (box_objs[1] > box_objs[3])) else box_objs
+        for box_objs in correct_bboxes
+    ]
+
+    correct_bboxes = [
+    box_obj if (
+            box_obj is not None and 
+            box_obj[0] >= transformed_page_bbox[0] and 
+            box_obj[1] >= transformed_page_bbox[1] and 
+            box_obj[2] <= transformed_page_bbox[2] and 
+            box_obj[3] <= transformed_page_bbox[3]
+            ) 
+            else None 
+    for box_obj in correct_bboxes
+    ]
+
+    return correct_bboxes
+    
+
+def transform_bbox(
+    page_bbox: list[float],
+    page_rotation: int,
+    bbox: tuple[float, float, float, float],
+) -> Bbox:
+    """
+    Transform pdfium bbox to device bbox
+    """
+    x_start, y_start, x_end, y_end = page_bbox
+
+    page_width = math.ceil(abs(x_end - x_start))
+    page_height = math.ceil(abs(y_end - y_start))
+    
+    cx_start, cy_start, cx_end, cy_end = bbox
+
+    cx_start -= x_start
+    cx_end -= x_start
+    cy_start -= y_start
+    cy_end -= y_start
+
+    ty_start = page_height - cy_start
+    ty_end = page_height - cy_end
+
+    bbox_coords = [
+        min(cx_start, cx_end),
+        min(ty_start, ty_end),
+        max(cx_start, cx_end),
+        max(ty_start, ty_end),
+    ]
+
+    return Bbox(bbox_coords).rotate(page_width, page_height, page_rotation)
+
+
+def flatten(page: pdfium.PdfPage, flag: int = pdfium_c.FLAT_NORMALDISPLAY) -> None:
     rc = pdfium_c.FPDFPage_Flatten(page, flag)
     if rc == pdfium_c.FLATTEN_FAIL:
         raise pdfium.PdfiumError("Failed to flatten annotations / form fields.")
 
 
-def get_fontname(textpage, i):
+def get_fontname(textpage: pdfium.PdfTextPage, i: int) -> Tuple[str, int]:
     font_name_str = ""
     flags = 0
     try:
@@ -42,16 +153,16 @@ def matrix_intersection_area(boxes1: List[List[float]], boxes2: List[List[float]
     if len(boxes1) == 0 or len(boxes2) == 0:
         return np.zeros((len(boxes1), len(boxes2)))
 
-    boxes1 = np.array(boxes1)
-    boxes2 = np.array(boxes2)
+    boxes1_np = np.array(boxes1)
+    boxes2_np = np.array(boxes2)
 
-    boxes1 = boxes1[:, np.newaxis, :]  # Shape: (N, 1, 4)
-    boxes2 = boxes2[np.newaxis, :, :]  # Shape: (1, M, 4)
+    boxes1_np = boxes1_np[:, np.newaxis, :]  # Shape: (N, 1, 4)
+    boxes2_np = boxes2_np[np.newaxis, :, :]  # Shape: (1, M, 4)
 
-    min_x = np.maximum(boxes1[..., 0], boxes2[..., 0])  # Shape: (N, M)
-    min_y = np.maximum(boxes1[..., 1], boxes2[..., 1])
-    max_x = np.minimum(boxes1[..., 2], boxes2[..., 2])
-    max_y = np.minimum(boxes1[..., 3], boxes2[..., 3])
+    min_x = np.maximum(boxes1_np[..., 0], boxes2_np[..., 0])  # Shape: (N, M)
+    min_y = np.maximum(boxes1_np[..., 1], boxes2_np[..., 1])
+    max_x = np.minimum(boxes1_np[..., 2], boxes2_np[..., 2])
+    max_y = np.minimum(boxes1_np[..., 3], boxes2_np[..., 3])
 
     width = np.maximum(0, max_x - min_x)
     height = np.maximum(0, max_y - min_y)
diff --git a/pdftext/postprocessing.py b/pdftext/postprocessing.py
index 4174149..fc12d62 100644
--- a/pdftext/postprocessing.py
+++ b/pdftext/postprocessing.py
@@ -1,8 +1,8 @@
 import unicodedata
-from typing import List
+from typing import List, Dict
 
 from pdftext.pdf.utils import LINE_BREAKS, SPACES, TABS, WHITESPACE_CHARS
-from pdftext.schema import Page
+from pdftext.schema import Page, Block
 
 LIGATURES = {
     "ff": "ff",
@@ -23,12 +23,13 @@ def postprocess_text(text: str) -> str:
     for old, new in REPLACEMENTS.items():
         text = text.replace(old, new)
     text = replace_special_chars(text)
+    text = fix_unicode_surrogate_pairs(text)
     text = replace_control_chars(text)
     text = replace_ligatures(text)
     return text
 
 
-def handle_hyphens(text: str, keep_hyphens=False) -> str:
+def handle_hyphens(text: str, keep_hyphens: bool = False) -> str:
     if keep_hyphens:
         text = text.replace(HYPHEN_CHAR, "-\n")
     elif len(text) == 0:
@@ -64,7 +65,15 @@ def replace_special_chars(text: str) -> str:
 
 
 def replace_control_chars(text: str) -> str:
-    return "".join(char for char in text if (unicodedata.category(char)[0] != "C" or char == HYPHEN_CHAR or char in WHITESPACE_CHARS))
+    return "".join(
+        char
+        for char in text
+        if (
+            unicodedata.category(char)[0] != "C"
+            or char == HYPHEN_CHAR
+            or char in WHITESPACE_CHARS
+        )
+    )
 
 
 def replace_ligatures(text: str) -> str:
@@ -73,9 +82,9 @@ def replace_ligatures(text: str) -> str:
     return text
 
 
-def sort_blocks(blocks: List, tolerance=1.25) -> List:
+def sort_blocks(blocks: List[Block], tolerance: float = 1.25) -> List[Block]:
     # Sort blocks into best guess reading order
-    vertical_groups = {}
+    vertical_groups: Dict[float, List[Block]] = {}
     for block in blocks:
         group_key = round(block["bbox"][1] / tolerance) * tolerance
         if group_key not in vertical_groups:
@@ -92,7 +101,7 @@ def sort_blocks(blocks: List, tolerance=1.25) -> List:
     return sorted_page_blocks
 
 
-def merge_text(page: Page, sort=False, hyphens=False) -> str:
+def merge_text(page: Page, sort: bool = False, hyphens: bool = False) -> str:
     text = ""
     if sort:
         page["blocks"] = sort_blocks(page["blocks"])
@@ -111,3 +120,53 @@ def merge_text(page: Page, sort=False, hyphens=False) -> str:
         text += block_text
     text = handle_hyphens(text, keep_hyphens=hyphens)
     return text
+
+
+def fix_unicode_surrogate_pairs(text: str) -> str:
+    """
+    Fix Unicode surrogate pairs while preserving mathematical symbols.
+
+    Surrogate pairs are UTF-16 artifacts that shouldn't appear in UTF-8.
+    This function converts them to proper Unicode characters.
+    """
+    if not text:
+        return ""
+
+    try:
+        # Test if the text is already valid UTF-8
+        text.encode("utf-8")
+        return text
+    except UnicodeEncodeError:
+        # Handle surrogate pairs by converting them to proper Unicode
+        result = []
+        i = 0
+        while i < len(text):
+            char = text[i]
+            code = ord(char)
+
+            # High surrogate followed by low surrogate = valid pair
+            if (
+                0xD800 <= code <= 0xDBFF
+                and i + 1 < len(text)
+                and 0xDC00 <= ord(text[i + 1]) <= 0xDFFF
+            ):
+
+                high = code - 0xD800
+                low = ord(text[i + 1]) - 0xDC00
+                unicode_point = 0x10000 + (high << 10) + low
+
+                try:
+                    result.append(chr(unicode_point))
+                    i += 2
+                    continue
+                except ValueError:
+                    pass
+
+            # Replace lone surrogates with replacement character
+            if 0xD800 <= code <= 0xDFFF:
+                result.append("\ufffd")
+            else:
+                result.append(char)
+            i += 1
+
+        return "".join(result)
diff --git a/pdftext/schema.py b/pdftext/schema.py
index 736b856..d720ca2 100644
--- a/pdftext/schema.py
+++ b/pdftext/schema.py
@@ -5,7 +5,7 @@
 
 
 class Bbox:
-    def __init__(self, bbox: List[float], ensure_nonzero_area=False):
+    def __init__(self, bbox: List[float], ensure_nonzero_area: bool = False) -> None:
         if ensure_nonzero_area:
             bbox = list(bbox)
             bbox[2] = max(bbox[0], bbox[2] + 1)
@@ -13,48 +13,57 @@ def __init__(self, bbox: List[float], ensure_nonzero_area=False):
         self.bbox = bbox
         self.ensure_nonzero_area = ensure_nonzero_area
 
-    def __getitem__(self, item):
+    def __getitem__(self, item: int) -> float:
         return self.bbox[item]
 
-    def __repr__(self):
+    def __repr__(self) -> str:
         return f"Bbox({self.bbox})"
 
     @property
-    def height(self):
+    def height(self) -> float:
         return self.bbox[3] - self.bbox[1]
 
     @property
-    def width(self):
+    def width(self) -> float:
         return self.bbox[2] - self.bbox[0]
 
     @property
-    def area(self):
+    def area(self) -> float:
         return self.width * self.height
 
     @property
-    def center(self):
+    def center(self) -> List[float]:
         return [(self.bbox[0] + self.bbox[2]) / 2, (self.bbox[1] + self.bbox[3]) / 2]
 
     @property
-    def size(self):
+    def size(self) -> List[float]:
         return [self.width, self.height]
 
     @property
-    def x_start(self):
+    def x_start(self) -> float:
         return self.bbox[0]
 
     @property
-    def y_start(self):
+    def y_start(self) -> float:
         return self.bbox[1]
 
     @property
-    def x_end(self):
+    def x_end(self) -> float:
         return self.bbox[2]
 
     @property
-    def y_end(self):
+    def y_end(self) -> float:
         return self.bbox[3]
 
+    def inside(self, other: Bbox) -> bool:
+        # Check if the self bounding box is entirely contained within the other bounding box.
+        return (
+            self.x_start >= other.x_start
+            and self.x_end <= other.x_end
+            and self.y_start >= other.y_start
+            and self.y_end <= other.y_end
+        )
+
     def merge(self, other: Bbox) -> Bbox:
         self_bbox = self.bbox
         other_bbox = other.bbox
@@ -65,18 +74,43 @@ def merge(self, other: Bbox) -> Bbox:
             max(self_bbox[3], other_bbox[3])
         ])
 
-    def overlap_x(self, other: Bbox):
-        return max(0, min(self.bbox[2], other.bbox[2]) - max(self.bbox[0], other.bbox[0]))
+    def overlap_x(self, other: Bbox) -> float:
+        return max(
+            0, min(self.bbox[2], other.bbox[2]) - max(self.bbox[0], other.bbox[0])
+        )
+
+    def overlap_y(self, other: Bbox) -> float:
+        return max(
+            0, min(self.bbox[3], other.bbox[3]) - max(self.bbox[1], other.bbox[1])
+        )
+
+    def horizontal_distance(self, other: Bbox) -> float:
+        x1, y1, x2, y2 = self.bbox
+        i1, j1, i2, j2 = other.bbox
+
+        if x1 < i1:
+            return i1 - x1
+        elif i2 < x1:
+            return x1 - i2
+
+        return 0
 
-    def overlap_y(self, other: Bbox):
-        return max(0, min(self.bbox[3], other.bbox[3]) - max(self.bbox[1], other.bbox[1]))
+    def intersection_score(self, other: Bbox) -> float:
+        intersection_area = self.intersection_area(other)
+        if self.area == 0 or other.area == 0:
+            if self.inside(other) or other.inside(self):
+                return 1.0
+            else:
+                return 0.0
+        else:
+            return intersection_area / min(self.area, other.area)
 
-    def intersection_area(self, other: Bbox):
+    def intersection_area(self, other: Bbox) -> float:
         return self.overlap_x(other) * self.overlap_y(other)
 
-    def intersection_pct(self, other: Bbox):
+    def intersection_pct(self, other: Bbox) -> float:
         if self.area == 0:
-            return 0
+            return 0.0
 
         intersection = self.intersection_area(other)
         return intersection / self.area
@@ -106,12 +140,12 @@ def rotate(self, page_width: float, page_height: float, rotation: int) -> Bbox:
             new_y_max = page_width - x_min
 
         # Ensure that x_min < x_max and y_min < y_max
-        rotated_bbox = (
+        rotated_bbox = [
             min(new_x_min, new_x_max),
             min(new_y_min, new_y_max),
             max(new_x_min, new_x_max),
-            max(new_y_min, new_y_max)
-        )
+            max(new_y_min, new_y_max),
+        ]
 
         return Bbox(rotated_bbox)
 
@@ -122,7 +156,7 @@ def rescale(self, img_size: List[int], page: Page) -> Bbox:
             self.bbox[0] * w_scale,
             self.bbox[1] * h_scale,
             self.bbox[2] * w_scale,
-            self.bbox[3] * h_scale
+            self.bbox[3] * h_scale,
         ]
 
         return Bbox(new_bbox)
@@ -168,7 +202,10 @@ class Page(TypedDict):
     height: int
     blocks: List[Block]
     rotation: int
-    refs: List[Reference]
+    refs: Optional[List[Reference]]
+    scale: int
+    page_image: str
+    images: List[Bbox]
 
 
 class TableCell(TypedDict):
@@ -196,16 +233,16 @@ class Reference:
     coord: List[float]
 
     @property
-    def ref(self):
+    def ref(self) -> str:
         return f"page-{self.page}-{self.idx}"
 
     @property
-    def url(self):
+    def url(self) -> str:
         return f"#{self.ref}"
 
 
 class PageReference:
-    def __init__(self):
+    def __init__(self) -> None:
         self.page_ref_map: Dict[int, List[Reference]] = {}
 
     def get_refs(self, page: int) -> List[Reference]:
diff --git a/pdftext/scripts/__init__.py b/pdftext/scripts/__init__.py
new file mode 100644
index 0000000..25f13e2
--- /dev/null
+++ b/pdftext/scripts/__init__.py
@@ -0,0 +1 @@
+"""pdftext.scripts package.""" 
\ No newline at end of file
diff --git a/pdftext/scripts/extract_text.py b/pdftext/scripts/extract_text.py
index cb8dd38..4c89e32 100644
--- a/pdftext/scripts/extract_text.py
+++ b/pdftext/scripts/extract_text.py
@@ -1,50 +1,107 @@
 import json
 from pathlib import Path
-from typing import List
+from typing import List, Any
 
 import click
 import pypdfium2 as pdfium
 
 from pdftext.extraction import plain_text_output, dictionary_output
+from pdftext.schema import Pages, Bbox
+
+
+# Helper function to serialize Bbox objects for JSON
+def json_serializer(obj: Any) -> Any:
+    if isinstance(obj, Bbox):
+        return obj.bbox
+    raise TypeError(f"Object of type {obj.__class__.__name__} is not JSON serializable")
+
 
 def parse_range_str(range_str: str) -> List[int]:
     range_lst = range_str.split(",")
-    page_lst = []
+    page_lst: List[int] = []
     for i in range_lst:
         if "-" in i:
             start, end = i.split("-")
-            page_lst += list(range(int(start), int(end) + 1))
+            page_lst.extend(list(range(int(start), int(end) + 1)))
         else:
             page_lst.append(int(i))
-    page_lst = sorted(list(set(page_lst)))  # Deduplicate page numbers and sort in order
+    # Deduplicate page numbers and sort in order
+    page_lst = sorted(list(set(page_lst)))
     return page_lst
 
+
 @click.command(help="Extract plain text or JSON from PDF.")
 @click.argument("pdf_path", type=click.Path(exists=True))
-@click.option("--out_path", type=click.Path(exists=False), help="Path to the output text file, defaults to stdout")
-@click.option("--json", is_flag=True, help="Output json instead of plain text", default=False)
-@click.option("--sort", is_flag=True, help="Attempt to sort the text by reading order", default=False)
-@click.option("--keep_hyphens", is_flag=True, help="Keep hyphens in words", default=False)
-@click.option("--page_range", type=str, help="Page numbers or ranges to extract, comma separated like 1,2-4,10", default=None)
-@click.option("--flatten_pdf", is_flag=True, help="Flatten form fields and annotations into page contents", default=False)
-@click.option("--keep_chars", is_flag=True, help="Keep character level information", default=False)
-@click.option("--workers", type=int, help="Number of workers to use for parallel processing", default=None)
+@click.option(
+    "--out_path",
+    type=click.Path(exists=False),
+    help="Path to the output text file, defaults to stdout"
+)
+@click.option(
+    "--json",
+    is_flag=True,
+    help="Output json instead of plain text",
+    default=False
+)
+@click.option(
+    "--sort",
+    is_flag=True,
+    help="Attempt to sort the text by reading order",
+    default=False
+)
+@click.option(
+    "--keep_hyphens",
+    is_flag=True,
+    help="Keep hyphens in words",
+    default=False
+)
+@click.option(
+    "--page_range",
+    type=str,
+    help="Page numbers or ranges to extract, comma separated like 1,2-4,10",
+    default=None
+)
+@click.option(
+    "--flatten_pdf",
+    is_flag=True,
+    help="Flatten form fields and annotations into page contents",
+    default=False
+)
+@click.option(
+    "--keep_chars",
+    is_flag=True,
+    help="Keep character level information",
+    default=False
+)
+@click.option(
+    "--workers",
+    type=int,
+    help="Number of workers to use for parallel processing",
+    default=None
+)
 def extract_text_cli(
         pdf_path: Path,
         out_path: Path | None,
-        **kwargs
-):
-    pages = None
+        **kwargs: Any
+) -> None:
+    pages: List[int] | None = None
     if kwargs["page_range"] is not None:
-        pdf_doc = pdfium.PdfDocument(pdf_path)
+        pdf_doc = pdfium.PdfDocument(str(pdf_path))
         pages = parse_range_str(kwargs["page_range"])
         doc_len = len(pdf_doc)
         pdf_doc.close()
-        assert all(0 <= p <= doc_len for p in pages), "Invalid page number(s) provided"
+        # Ensure page numbers are 1-based for user input validation
+        invalid_pages = [p for p in pages if not (1 <= p <= doc_len)]
+        if invalid_pages:
+            raise click.BadParameter(
+                f"Invalid page number(s) provided: {invalid_pages}. "
+                f"Document has {doc_len} pages."
+            )
 
+    output_text: str = ""
     if kwargs["json"]:
-        text = dictionary_output(
-            pdf_path,
+        dict_result: Pages = dictionary_output(
+            str(pdf_path),
             sort=kwargs["sort"],
             page_range=pages,
             flatten_pdf=kwargs["flatten_pdf"],
@@ -52,10 +109,13 @@ def extract_text_cli(
             workers=kwargs["workers"],
             disable_links=True
         )
-        text = json.dumps(text, ensure_ascii=False, indent=2)
+        # Convert Pydantic model to dict for JSON serialization
+        # output_text = json.dumps(dict_result.model_dump())
+        # Use the custom serializer for Bbox objects
+        output_text = json.dumps(dict_result, default=json_serializer)
     else:
-        text = plain_text_output(
-            pdf_path,
+        output_text = plain_text_output(
+            str(pdf_path),
             sort=kwargs["sort"],
             hyphens=kwargs["keep_hyphens"],
             page_range=pages,
@@ -64,7 +124,7 @@ def extract_text_cli(
         )
 
     if out_path is None:
-        print(text)
+        print(output_text)
     else:
-        with open(out_path, "w+", encoding="utf-8") as f:
-            f.write(text)
\ No newline at end of file
+        with open(str(out_path), "w+", encoding="utf-8") as f:
+            f.write(output_text)
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
deleted file mode 100644
index 01b26a5..0000000
--- a/poetry.lock
+++ /dev/null
@@ -1,2116 +0,0 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
-
-[[package]]
-name = "aiohappyeyeballs"
-version = "2.4.6"
-description = "Happy Eyeballs for asyncio"
-optional = false
-python-versions = ">=3.9"
-files = [
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-    {file = "aiohappyeyeballs-2.4.6.tar.gz", hash = "sha256:9b05052f9042985d32ecbe4b59a77ae19c006a78f1344d7fdad69d28ded3d0b0"},
-]
-
-[[package]]
-name = "aiohttp"
-version = "3.11.12"
-description = "Async http client/server framework (asyncio)"
-optional = false
-python-versions = ">=3.9"
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-]
-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
-propcache = ">=0.2.0"
-
-[metadata]
-lock-version = "2.0"
-python-versions = "^3.10"
-content-hash = "ddd1896418384a59ea5cc93165805531aaa09c17748180768da7266c57c8307e"
diff --git a/pyproject.toml b/pyproject.toml
index e7eb36a..a6da4fa 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,35 +1,60 @@
-[tool.poetry]
+[project]
 name = "pdftext"
 version = "0.6.3"
 description = "Extract structured text from pdfs quickly"
-authors = ["Vik Paruchuri <vik.paruchuri@gmail.com>"]
-license = "Apache-2.0"
+authors = [
+    {name = "Vik Paruchuri", email = "vik.paruchuri@gmail.com"}
+]
+license = {text = "Apache-2.0"}
 readme = "README.md"
-repository = "https://github.com/VikParuchuri/pdftext"
+requires-python = ">=3.10"
 keywords = ["pdf", "text", "extraction"]
-packages = [
-    {include = "pdftext"}
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "License :: OSI Approved :: Apache Software License",
+    "Operating System :: OS Independent",
+]
+dependencies = [
+    "click>=8.1.8",
+    "pypdfium2==4.30.0",
+    "pydantic>=2.7.1,<3.0.0",
+    "pydantic-settings>=2.2.1,<3.0.0",
+    "numpy>=2.2.4",
+    "pillow>=10.3.0,<11.0.0",
+    "pytest>=8.3.5",
+    "mypy>=1.15.0",
+    "aiofiles>=24.1.0",
 ]
 
-[tool.poetry.dependencies]
-python = "^3.10"
-pypdfium2 = "=4.30.0"
-pydantic = "^2.7.1"
-pydantic-settings = "^2.2.1"
-
-click = "^8.1.8"
-[tool.poetry.group.dev.dependencies]
-pymupdf = "^1.24.2"
-datasets = "^2.19.0"
-pdfplumber = "^0.11.0"
-pillow = "^10.3.0"
-rapidfuzz = "^3.8.1"
-tabulate = "^0.9.0"
-
-pytest = "^8.3.4"
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
+[project.urls]
+Repository = "https://github.com/VikParuchuri/pdftext"
 
-[tool.poetry.scripts]
+[project.scripts]
 pdftext = "pdftext.scripts.extract_text:extract_text_cli"
+
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools]
+packages = ["pdftext", "pdftext.scripts", "pdftext.pdf"]
+
+[tool.pytest.ini_options]
+minversion = "6.0"
+addopts = "-ra -q"
+testpaths = [
+    "tests",
+]
+
+[tool.mypy]
+# Default mypy settings can go here if needed in the future
+
+[[tool.mypy.overrides]]
+module = "pdftext.tables"
+ignore_errors = true
+
+[project.optional-dependencies]
+dev = ["aiofiles"]
diff --git a/requirements-dev.txt b/requirements-dev.txt
new file mode 100644
index 0000000..0bb3211
--- /dev/null
+++ b/requirements-dev.txt
@@ -0,0 +1,8 @@
+-r requirements.txt
+pymupdf>=1.24.2,<2.0.0
+datasets>=2.19.0,<3.0.0
+pdfplumber>=0.11.0,<0.12.0
+pillow>=10.3.0,<11.0.0
+rapidfuzz>=3.8.1,<4.0.0
+tabulate>=0.9.0,<0.10.0
+pytest>=8.3.4,<9.0.0 
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..a6a7fed
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,4 @@
+pypdfium2==4.30.0
+pydantic>=2.7.1,<3.0.0
+pydantic-settings>=2.2.1,<3.0.0
+click>=8.1.8,<9.0.0 
\ No newline at end of file
diff --git a/test.py b/test.py
new file mode 100644
index 0000000..fe8a5ac
--- /dev/null
+++ b/test.py
@@ -0,0 +1,364 @@
+from pdftext.extraction import dictionary_output
+import argparse
+import json
+import pypdfium2 as pdfium
+import asyncio
+import aiofiles
+import io
+import base64
+import os
+from pathlib import Path
+from typing import List, Optional, Dict, Any, Union, Tuple
+from PIL import Image, ImageDraw
+from pdftext.schema import Bbox
+
+
+def parse_range_str(range_str: str) -> List[int]:
+    """Parse a string of page ranges into a list of page numbers.
+
+    Examples:
+        "1,2-4,10" -> [0, 1, 2, 3, 9]  # 0-indexed
+    """
+    pages = []
+    for part in range_str.split(","):
+        if "-" in part:
+            start, end = map(int, part.split("-"))
+            pages.extend(range(start - 1, end))  # Convert to 0-indexed
+        else:
+            pages.append(int(part) - 1)  # Convert to 0-indexed
+    return pages
+
+
+def extract_text_from_pdf(
+    pdf_path: str,
+    sort: bool = False,
+    page_range: Optional[str] = None,
+    flatten_pdf: bool = False,
+    keep_chars: bool = False,
+    keep_hyphens: bool = False,
+    workers: Optional[int] = None,
+) -> List[Dict[str, Any]]:
+    """Extract text from PDF synchronously using dictionary_output."""
+    pdf_path = Path(pdf_path)
+
+    # Extract page range if provided
+    pages = None
+    if page_range is not None:
+        pdf_doc = pdfium.PdfDocument(pdf_path)
+        pages = parse_range_str(page_range)
+        doc_len = len(pdf_doc)
+        pdf_doc.close()
+        if not all(0 <= p < doc_len for p in pages):
+            raise ValueError("Invalid page number(s) provided")
+
+    # Extract text using dictionary_output
+    extracted_data = dictionary_output(
+        pdf_path,
+        sort=sort,
+        page_range=pages,
+        flatten_pdf=flatten_pdf,
+        keep_chars=keep_chars,
+        workers=workers,
+        disable_links=True,
+    )
+
+    return extracted_data
+
+
+def preprocess_image(page_image: Union[str, io.BytesIO]) -> Image.Image:
+    """Preprocess the page image from base64 string or BytesIO object."""
+    if isinstance(page_image, io.BytesIO):
+        image = Image.open(page_image)
+    else:
+        image_data = base64.b64decode(page_image)
+        image = Image.open(io.BytesIO(image_data))
+    return image
+
+
+def resize_image(image: Image.Image, max_size: int) -> Image.Image:
+    """Resize image if it exceeds the maximum size."""
+    # Calculate current size in bytes (approximate)
+    current_size = image.width * image.height * len(image.getbands())
+
+    if current_size <= max_size:
+        return image
+
+    # Calculate scale factor to reduce to max_size
+    scale_factor = (max_size / current_size) ** 0.5
+    new_width = int(image.width * scale_factor)
+    new_height = int(image.height * scale_factor)
+
+    return image.resize((new_width, new_height), Image.LANCZOS)
+
+
+def get_encoded_image(pil_image: Image.Image) -> str:
+    """Convert PIL image to base64 encoded string."""
+    max_size = 3 * 1024 * 1024  # 3 MB
+    pil_image = resize_image(pil_image, max_size)
+    img_bytes = io.BytesIO()
+    pil_image.save(img_bytes, format="PNG")
+    img_bytes = img_bytes.getvalue()
+    encoded_img = base64.b64encode(img_bytes).decode("utf-8")
+    return encoded_img
+
+
+def rescale_bbox(
+    src_bbox: List[float], dst_bbox: List[float], bbox: List[float]
+) -> List[float]:
+    """Rescale a bounding box from one coordinate system to another."""
+    src_width = src_bbox[2] - src_bbox[0]
+    src_height = src_bbox[3] - src_bbox[1]
+    dst_width = dst_bbox[2] - dst_bbox[0]
+    dst_height = dst_bbox[3] - dst_bbox[1]
+
+    x_scale = dst_width / src_width
+    y_scale = dst_height / src_height
+
+    return [
+        dst_bbox[0] + (bbox[0] - src_bbox[0]) * x_scale,
+        dst_bbox[1] + (bbox[1] - src_bbox[1]) * y_scale,
+        dst_bbox[0] + (bbox[2] - src_bbox[0]) * x_scale,
+        dst_bbox[1] + (bbox[3] - src_bbox[1]) * y_scale,
+    ]
+
+
+def union_bbox(bbox1: Optional[List[float]], bbox2: List[float]) -> List[float]:
+    """Compute the union of two bounding boxes."""
+    if bbox1 is None:
+        return bbox2
+    return [
+        min(bbox1[0], bbox2[0]),
+        min(bbox1[1], bbox2[1]),
+        max(bbox1[2], bbox2[2]),
+        max(bbox1[3], bbox2[3]),
+    ]
+
+
+def visualize_bboxes(
+    page: Dict[str, Any],
+    text_color: str = "red",
+    image_color: str = "blue",
+    crop_flag: bool = True,
+) -> Tuple[str, Image.Image]:
+    """Draw bounding boxes on the page image and return the encoded image and PIL Image."""
+    # Extract all bboxes from the page
+    text_bboxes = []
+    image_bboxes = []
+
+    blocks = page.get("blocks", [])
+    page_image = page.get("page_image", "")
+    width = page.get("width", 0)
+    height = page.get("height", 0)
+
+    for block in blocks:
+        # all_bboxes.append(block.get('bbox', []))
+        for line in block.get("lines", []):
+            # all_bboxes.append(line.get('bbox', []))
+            for span in line.get("spans", []):
+                text_bboxes.append(span.get("bbox", []))
+
+    images = page.get("images", [])
+    for image in images:
+        image_bboxes.append(image.bbox)
+
+    img = preprocess_image(page_image)
+    img_bbox = [0, 0, img.size[0], img.size[1]]
+
+    # Scale page bboxes to image coordinates
+    page_bbox = [0, 0, width, height]
+    scaled_text_bboxes = [
+        rescale_bbox(page_bbox, img_bbox, bbox) for bbox in text_bboxes
+    ]
+    scaled_image_bboxes = [
+        rescale_bbox(page_bbox, img_bbox, bbox) for bbox in image_bboxes
+    ]
+    all_scaled_bboxes = scaled_text_bboxes + scaled_image_bboxes
+
+    # Draw boxes on the image
+    draw = ImageDraw.Draw(img)
+    for box in scaled_text_bboxes:
+        draw.rectangle(box, outline=text_color, width=1)
+    for box in scaled_image_bboxes:
+        draw.rectangle(box, outline=image_color, width=1)
+
+    if crop_flag and all_scaled_bboxes:
+        # Find the union of all bboxes
+        missing_boxes_bound = None
+        for box in all_scaled_bboxes:
+            missing_boxes_bound = union_bbox(missing_boxes_bound, box)
+
+        # Add some padding
+        padding = img.height * 0.05
+        crop = (
+            max(0, missing_boxes_bound[0] - padding),
+            max(0, missing_boxes_bound[1] - padding),
+            min(img.width, missing_boxes_bound[2] + padding),
+            min(img.height, missing_boxes_bound[3] + padding),
+        )
+
+        # Ensure minimum height
+        if crop[3] - crop[1] < img.height * 0.1:
+            diff = img.height * 0.1 - (crop[3] - crop[1])
+            crop = (
+                crop[0],
+                max(0, crop[1] - diff / 2),
+                crop[2],
+                min(img.height, crop[3] + diff / 2),
+            )
+
+        img = img.crop(crop)
+
+    return get_encoded_image(img), img
+
+
+async def save_visualization_image(
+    image: Image.Image, output_path: str, page_num: int
+) -> str:
+    """Save visualization image to disk and return the file path."""
+    # Create directory if it doesn't exist
+    os.makedirs(output_path, exist_ok=True)
+
+    # Generate filename
+    filename = f"page_{page_num+1}_visualization.png"
+    filepath = os.path.join(output_path, filename)
+
+    # Save image
+    image.save(filepath)
+    return filepath
+
+
+async def process_pages_with_ocr(
+    extracted_data: List[Dict[str, Any]],
+    visualize: bool = False,
+    viz_output_dir: Optional[str] = None,
+) -> Dict[str, Any]:
+    """Process extracted pages through OCR asynchronously."""
+    results = []
+    visualization_paths = []
+
+    for page_idx, page_data in enumerate(extracted_data):
+
+        # Visualize bounding boxes if requested
+        if visualize:
+            _, pil_img = visualize_bboxes(page_data)
+
+            # Always save visualization as separate file
+            output_dir = viz_output_dir or os.path.join(
+                os.path.dirname(os.path.abspath(__file__)), "visualizations"
+            )
+            filepath = await save_visualization_image(pil_img, output_dir, page_idx)
+            visualization_paths.append(filepath)
+            # Count lines in dictionary format
+            num_lines = sum(
+                len(block.get("lines", [])) for block in page_data.get("blocks", [])
+            )
+            print(
+                f"Page {page_idx+1}: Found {num_lines} lines, saved visualization to {filepath}"
+            )
+
+    output = {"pages": results}
+    if visualize and visualization_paths:
+        output["visualization_paths"] = visualization_paths
+
+    return output
+
+
+class BboxEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, Bbox):
+            return obj.bbox
+        return super().default(obj)
+
+
+async def save_output(
+    output_data: Dict[str, Any], out_path: Optional[str] = None
+) -> None:
+    """Save output data to a file or print to stdout."""
+    if out_path:
+        async with aiofiles.open(out_path, "w") as f:
+            await f.write(json.dumps(output_data, cls=BboxEncoder))
+    else:
+        print(json.dumps(output_data, cls=BboxEncoder))
+
+
+async def main():
+    parser = argparse.ArgumentParser(
+        description="Extract plain text or JSON from PDF and process with OCR."
+    )
+    parser.add_argument("pdf_path", type=str, help="Path to the input PDF file")
+    parser.add_argument(
+        "--out_path", type=str, help="Path to the output file, defaults to stdout"
+    )
+    parser.add_argument(
+        "--json",
+        dest="json_output",
+        action="store_true",
+        help="Output json instead of plain text",
+    )
+    parser.add_argument(
+        "--sort", action="store_true", help="Attempt to sort the text by reading order"
+    )
+    parser.add_argument(
+        "--keep_hyphens", action="store_true", help="Keep hyphens in words"
+    )
+    parser.add_argument(
+        "--page_range",
+        type=str,
+        help="Page numbers or ranges to extract, comma separated like 1,2-4,10",
+    )
+    parser.add_argument(
+        "--flatten_pdf",
+        action="store_true",
+        help="Flatten form fields and annotations into page contents",
+    )
+    parser.add_argument(
+        "--keep_chars", action="store_true", help="Keep character level information"
+    )
+    parser.add_argument(
+        "--workers", type=int, help="Number of workers to use for parallel processing"
+    )
+    parser.add_argument(
+        "--visualize",
+        action="store_true",
+        help="Visualize bounding boxes on the page image",
+    )
+    parser.add_argument(
+        "--viz_output_dir",
+        type=str,
+        help="Directory to save visualization images (defaults to ./visualizations)",
+    )
+
+    args = parser.parse_args()
+
+    try:
+        # Extract text synchronously
+        extracted_data = extract_text_from_pdf(
+            pdf_path=args.pdf_path,
+            sort=args.sort,
+            page_range=args.page_range,
+            flatten_pdf=args.flatten_pdf,
+            keep_chars=args.keep_chars,
+            keep_hyphens=args.keep_hyphens,
+            workers=args.workers,
+        )
+
+        # Process with OCR asynchronously
+        ocr_results = await process_pages_with_ocr(
+            extracted_data, visualize=args.visualize, viz_output_dir=args.viz_output_dir
+        )
+
+        # Save or print results
+        if args.json_output:
+            # If JSON output is requested, serialize extracted_data instead of ocr_results
+            await save_output({"pages": extracted_data}, args.out_path)
+        else:
+            await save_output(ocr_results, args.out_path)
+    except Exception as e:
+        print(f"Error processing PDF: {e}")
+        import traceback
+
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/test.sh b/test.sh
new file mode 100644
index 0000000..dfc0321
--- /dev/null
+++ b/test.sh
@@ -0,0 +1,53 @@
+#!/bin/bash
+
+FILE_NAME="deepseek_1"
+# Default values
+INPUT_FILE="./input/${FILE_NAME}.pdf"
+OUTPUT_JSON="./output/${FILE_NAME}_bbox.json"
+VIZ_DIR="./output/${FILE_NAME}"
+LOG_FILE="./output/${FILE_NAME}.log"
+VISUALIZE="--visualize"
+
+# Help function
+show_help() {
+  echo "Usage: ./run.sh [options]"
+  echo "Options:"
+  echo "  -i, --input FILE       Input PDF file (default: $INPUT_FILE)"
+  echo "  -o, --output FILE      Output JSON file (default: $OUTPUT_JSON)"
+  echo "  -v, --viz-dir DIR      Visualization output directory (default: $VIZ_DIR)"
+  echo "  -n, --no-visualize     Disable visualization"
+  echo "  -h, --help             Show this help message"
+  exit 1
+}
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+  case "$1" in
+    -i|--input)
+      INPUT_FILE="$2"
+      shift 2
+      ;;
+    -o|--output)
+      OUTPUT_JSON="$2"
+      shift 2
+      ;;
+    -v|--viz-dir)
+      VIZ_DIR="$2"
+      shift 2
+      ;;
+    -n|--no-visualize)
+      VISUALIZE=""
+      shift
+      ;;
+    -h|--help)
+      show_help
+      ;;
+    *)
+      echo "Unknown option: $1"
+      show_help
+      ;;
+  esac
+done
+
+# Run the parser with the specified or default options
+python3 test.py "$INPUT_FILE" --json --out_path "$OUTPUT_JSON" --viz_output_dir "$VIZ_DIR" $VISUALIZE > "$LOG_FILE"
diff --git a/tests/test_extraction.py b/tests/test_extraction.py
index dc8fdde..6802245 100644
--- a/tests/test_extraction.py
+++ b/tests/test_extraction.py
@@ -7,7 +7,7 @@ def test_paginated_output(pdf_path, pdf_doc):
     assert len(text) == len(pdf_doc)
     assert "Subspace" in text[0]
 
-def text_plain_text_output(pdf_path):
+def test_plain_text_output(pdf_path):
     text = plain_text_output(pdf_path)
     assert "Subspace" in text
 
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new file mode 100644
index 0000000..d0663de
--- /dev/null
+++ b/uv.lock
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