OCR is not magic image-to-text
When a document is scanned, the letters you see are pixels rather than actual text inside the file. OCR tries to recognize those pixels and build new text. That is why the quality of the source image matters so much.
Clarity matters more than file size
A clean, straight page gives OCR a much better chance. Tilted pages, shadows, colored backgrounds, and faint printing all make recognition harder. If you can rescan a page, making it straight and clean is often more valuable than relying on heavy processing later.
Arabic has its own challenges
Arabic brings its own challenges: connected letters, dots, diacritics, and mixtures of Arabic, numbers, and English can all affect recognition. It is normal to review extracted text, especially names, numbers, and dates. OCR is a way to reduce manual work, not a promise that every character is perfect.
The most useful way to use OCR
OCR is useful when you want an old document to become searchable or when you need to extract a large amount of text instead of typing it. For a final document meant for publication, treat the OCR result as a draft that deserves review.
A quick quality test
Test one page first. Search for a clear word, a long number, and a heading. If all three survive correctly, that is encouraging. If errors appear everywhere, improve the source images or language settings before processing the whole document.
Keep the page image as a proofreading reference
Even when extracted text looks excellent, keep the original page beside it while proofreading. That makes names, numbers, and punctuation easier to verify and prevents the OCR output from being mistaken for the legal or academic source document.