Recoding in 5 python modules
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- 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)
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- 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)
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- 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
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- 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
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- For pdf:
  - Concatenate pdf pages
  - Converte to pdf/1-a
  - Verify conformity to pdf/1-a
    

    
New temp folder structure
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  - 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)