The aim of this repo is to compare some tools to process pdf into text. The tools are :

• pdfplumber : made by jsvine for detailed information about each char, rectangle, line, et cetera — and easily extract text and tables.
• pdftotext : A simple PDF text extraction.
• pymupdf: a high-performance Python library for data extraction, analysis, conversion & manipulation of PDF (and other) documents.
• pypdf2: A pure-python PDF library capable of splitting, merging, cropping, and transforming PDF files.
• unstructured: an Open source libraries and APIs to build custom preprocessing pipelines for labeling, training, or production machine learning pipelines.

To compare the libraries, we used 3 PDF files:

• multicolunm-code-travail-cmr.pdf: A multicolumn file containing the Cameroonian Labor Code
• wtables-benin-LF-2023.pdf: A conventional PDF with some tables
• wtables-food-calories.pdf: Another conventional PDF with some tables

For each library we extract the text from all files. Then give an annotation on a scale of 1 to 5 for each comparison criteria.

• total time: Total time to process all the files;
• Conventional text: How well the tool performs on conventional text
• Multicolumn: How well the tool performs on multicolumn pdf document
• Table: How well the tool process simple tables (each table line on 1 document) in pdf documents
• Complex Table: How well the tool process complex tables (some table lines are on multiple document lines)
• Consistancy: How well the tool is viable (performs the same good result on different documents)

Following, is the comparison table of the results