OCRmyPDF/ROADMAP.md

Complete tool reimplementation in python Segregate the processing into 5 main tasks

• Normalize inputs (inputs can be a pdf file, an image, a folder containing images)
  • For pdf:
    • Identify if image already contains fonts
    • If the page needs to be OCRed:
      • Extract the image corresponding to the page and save it in a tmp folder. 3 approaches to extract images:
        • extract raw image from pdf and rotate it according to pdf page rotation
        • if not possible: identify resolution and rasterize
        • if not possible: use default resolution and rasterize
    • If not:
      • Save the page AS-IS in the tmp folder that should contained the final page
  • For image(s):
    • Just copy the images with standardized name into the tmp folder containing pages to be OCRed
• Preprocess normalized inputs (perform jobs in parallel)
  • Orientation (if requested by user)
    • Correct orientation
  • Skew angle (if requested by user)
    • Correct skew angle
  • Cleaning (if requested by user)
    • Clean image
• Perform OCR (perform jobs in parallel)
  • Perform OCR and save resulting hocr file for each respective page (perfom jobs in parallel)
• Generate output for each page
  • For pdf (if output file has a "pdf" extension):
    • Generate pdf pages from hocr files (note: pdf pages can already exist if OCR has been skipped for them)
• Build final output:
  • For pdf:
    • Concatenate pdf pages
    • Converte to pdf/1-a
    • Verify conformity to pdf/1-a

New temp folder structure:

• tmp_xxxxx/
  • a_raw_images (either from images or extracted from pdf file)
    • page_.
  • b_preprocessed_images (after deswing anf cleaning)
    • page_.
  • c_hocr_files
    • page_.hocr
  • d_output_pages (one file per page (at first 1 pdf file per page. Later on other formats might be supported)
    • page_.
  • e_final_output (concatenate output pages and conversion into PDF/1-a standard, Later on support other formats)
    • final.