PDF Extract
A lightweight Python library for extracting text characters and metadata from PDF files using PDFium. This library provides detailed character-level information including positions, fonts, colors, and transformation matrices without external dependencies.
Features
- Character-level extraction: Get individual characters with precise positioning
- Font metadata: Extract font information, sizes, and styling
- Color information: Access text colors (RGBA values)
- Transformation matrices: Retrieve text transformation data
- Page boundaries: Get page dimensions and bounding boxes
- Zero dependencies: Uses PDFium directly with no external requirements
- PyArrow integration: Optional PyArrow table output for data analysis
Installation
pip install pdf-extract
Basic Usage
Simple Character Extraction
from unpdf import extract
# Extract data from PDF
pages, chars, text_objs, fonts = extract("document.pdf")
# Access character data
print(f"Total characters: {len(chars.arrays['char'])}")
print(f"Total text objects: {len(text_objs.arrays['txt_obj_id'])}")
print(f"Total fonts: {len(fonts.arrays['font_obj_id'])}")
# Get page information
print(f"Page width: {pages.arrays['width'][0]}")
print(f"Page height: {pages.arrays['height'][0]}")
Reconstructing Text
from unpdf import extract
_, chars, _, _ = extract("document.pdf")
# Convert characters to string
text = ''.join(chr(c) for c in chars.arrays['char'])
print(text)
Character Positioning
from unpdf import extract
_, chars, _, _ = extract("document.pdf")
# Access character positions
for i in range(len(chars.arrays['char'])):
char = chr(chars.arrays['char'][i])
left = chars.arrays['left'][i]
top = chars.arrays['top'][i]
right = chars.arrays['right'][i]
bottom = chars.arrays['bottom'][i]
print(f"'{char}' at ({left:.1f}, {top:.1f}) - ({right:.1f}, {bottom:.1f})")
PyArrow / pandas / polars Integration
For data analysis and advanced processing, it is recommended to convert to first convert to PyArrow tables. Note that PyArrow must be installed separately. From PyArrow tables, you can easily convert to pandas or polars DataFrames.
from unpdf import extract
pages, chars, text_objs, fonts = extract("document.pdf")
# Convert to PyArrow tables
pages_table = pages.table
chars_table = chars.table
text_objs_table = text_objs.table
fonts_table = fonts.table
# Use PyArrow functionality
print(chars_table.schema)
print(chars_table.select(['char', 'left', 'top']))
# Export to different dataframe libraries
import polars as pl
pandas_char_table = chars_table.to_pandas() # Convert to pandas DataFrame
polars_char_table = pl.from_arrow(chars_table) # Convert to polars DataFrame
Data Structure
The library returns four table objects:
- PageTable: Page dimensions and boundaries
- CharTable: Individual characters with positions and metadata
- TextObjTable: Text object properties (font size, color, transformations)
- FontTable: Font information and styling
Available Fields
PageTable fields:
page: Page numberwidth,height: Page dimensionsleft,right,top,bottom: Page boundaries
CharTable fields:
page: Page numberchar: Unicode character codeis_generated: Whether character is generated by PDFiumtxt_obj_id: Reference to text objectleft,right,top,bottom: Character bounding box (precise)loose_left,loose_right,loose_top,loose_bottom: Loose character bounding boxbbox_ok: Whether precise bounding box is validloose_bbox_ok: Whether loose bounding box is validhyphen: Hyphen indicatorhas_unicode_map_error: Whether character has unicode mapping error
TextObjTable fields:
txt_obj_id: Unique text object identifierfontsize: Font sizehas_transparency: Whether text object has transparencyfont_obj_id: Reference to font objectcolor_R,color_G,color_B,color_A: RGBA color valuestmatrix_a,tmatrix_b,tmatrix_c,tmatrix_d,tmatrix_e,tmatrix_f: Transformation matrix components
FontTable fields:
font_obj_id: Unique font identifierflags: Font flagsweight: Font weightitalic_angle: Italic anglebase_fontname,family_fontname: Font names
Requirements
- Python 3.7+
- PyArrow (optional, for table functionality)
License
This project is licensed under the Apache 2.0 license.
Release files for unPDF 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
Total release size: 120.1 MB
Release files / unpdf-1.0.0-cp313-cp313-win_arm64.whl
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| Size | 2.9 MB |
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