PDF Text Extractor

Project roadmap

1. 01

   Define the inputs and outputs

   ~1h

   Write README.md: the command (pdftext INPUT_FOLDER OUTPUT_FOLDER, with --min-chars for the low-text threshold per page, --workers and --resume), the outputs (one .txt per PDF with page separators, report.csv with one row per page, summary.csv with one row per document, and thumbnails of low-text pages), and how errors are recorded. Create a virtual environment with pypdf, pandas, Pillow and pypdfium2 (for rendering pages). Done when: README.md defines the command and every output.

2. 02

   Extract text from one PDF

   ~2h

   Write a function that opens a PDF with pypdf, handles encrypted files (trying an empty password, otherwise recording it as encrypted), extracts each page's text, and returns per-page results (page number, text and character count). Catch errors from damaged files and damaged pages separately, so one bad page doesn't lose the rest. Done when: text is extracted from sample PDFs, and damaged or encrypted ones are recorded with a reason.

3. 03

   Process a whole folder

   ~2h

   Find all PDFs in the input folder, and process them in parallel with concurrent.futures.ProcessPoolExecutor (limited by --workers), writing each document's text file as soon as it's done (to a temporary name, then renaming). Keep a progress file of finished documents, so --resume skips them after an interruption. Show progress. Done when: a folder of PDFs is processed in parallel, and a stopped run resumes without redoing finished files.

4. 04

   Build the report with pandas

   ~1.5h

   Collect per-page results into a pandas DataFrame, mark pages below --min-chars as low text, and write report.csv. Build summary.csv per document with pages, total characters, low-text pages and their share, and status (ok, partial, encrypted or error), sorted by the share of low-text pages. Print the documents most likely to need OCR. Done when: the reports list correct numbers for the sample PDFs.

5. 05

   Make thumbnails of suspicious pages

   ~1.5h

   For low-text pages, render the page to an image with pypdfium2, shrink it with Pillow to a small thumbnail, and save it as PNG named after the document and page. Add an optional HTML page listing the thumbnails with their document names, for a quick visual check. Done when: thumbnails of scanned pages are created and look right.

6. 06

   Test it

   ~1.5h

   Write pytest tests with small generated PDFs (a text PDF made with a library such as reportlab or fpdf2, an image-only PDF, an encrypted one and a truncated file): extraction, low-text detection, error recording, resume behavior and the report contents. Done when: pytest passes.

Resources

• Docspypdf documentation
• Docspypdfium2
• Docspandas user guide
• DocsPillow documentation
• DocsPython concurrent.futures