OCRmyPDF/ROADMAP.md

Recoding in 5 python modules

• Less platform dependent implementation
• Higher versality (wrt addition of new intput / output file tytes)

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