Improve efficiency on PDFs which contain large amounts of text about pdf2pdfocr HOT 5 CLOSED

Yes, I see a 9X improvement on speed with the new flag for documents of moderate text size and low image count (30 pages).

Thank you for the feature!

Testing on this document with 30 pages of text: https://raw.githubusercontent.com/liquidinvestigations/hoover-testdata/master/data/disk-files/pdf-doc-txt/stanley.ec02.pdf

time pdf2pdfocr.py -v -a -l eng -x '--oem 1 --psm 1' -j 0.01  -i document.pdf
real    1m53.855s                                                                                                              
user    1m48.059s 

time pdf2pdfocr.py -v -a -l eng -x '--oem 1 --psm 1' -j 0.01 --ignore-existing-text -i document.pdf 
real    0m13.981s                                                                                                              
user    0m12.642s  

Thanks again!

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Hello, @gabriel-v.
Great use case. Thank you.
By now, I think it would be simpler if script remove all known text before start OCR.
I'll check on this.

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I think a good try is here: https://stackoverflow.com/questions/24322338/remove-all-text-from-pdf-file

input.pdf

I tested manually with a 162 page text PDF with one single image on page 1 (see attached file).

No text in PDF (first page with image and 161 blank pages)
[2022-05-13 09:09:22.666163] [LOG] Success in 71.173 seconds!

Normal PDF (162 pages with text)
[2022-05-13 09:21:12.134621] [LOG] Success in 607.781 seconds!

My first conclusion is: tesseract is fast with blank pages.

Maybe we can optimize even more detecting blank pages and avoid calling tesseract for them. Method "do_check_img_greyscale" can be used as example.

This use case is interesting. I'll code this! :-)

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Please let me know if last commit works for you.

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[2022-05-15 09:24:48.847586] [LOG] Success in 16.423 seconds!

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