The complete guide to extracting text and tables from scanned PDFs

Scanned documents, paper receipts and photographed textbook pages are stored in PDFs as flat images. You cannot select words, copy paragraphs, search for keywords or pull out the accounting figures. Optical Character Recognition (OCR) turns those pixels back into text you can edit and search. That is what OCR PDF does, on your own machine.

How the OCR Pipeline Extracts Text from Scans

A serious OCR engine takes each scanned page through four main stages:

• Image Preprocessing: Turns colour scans into high-contrast black-and-white images, straightens tilted pages and removes dust specks.
• Layout Analysis: Works out the reading order, separates text columns, and tells body paragraphs apart from tables, headers and photographs.
• Character & Word Recognition: A neural network identifies the shape of each character and checks the resulting words against a dictionary model for the language.
• Searchable PDF Generation: Adds an invisible, selectable text layer placed exactly over the original scanned image, which gives you a "Searchable PDF" (PDF/A).

Top Tips for Maximizing OCR Extraction Accuracy

1. Scan at 300 DPI Resolution: 300 DPI gives the best balance between clear character strokes and memory use (Tesseract's own guide on improving OCR quality also recommends at least 300 DPI). Below 150 DPI, the loops of letters break up, and 'e' can be read as 'c'.
2. Flatten Document Pages: Remove the curved shadows that book spines cause. Curved lines distort the shape of the characters.
3. Select the Correct Language Dictionary: When you set the right language, the engine can use that language's grammar and vocabulary to decide between characters that look alike. Tesseract ships separate trained data files for each language for this reason.

If the PDF turns out not to be a scan after all (it holds real text that you simply could not select), skip OCR and pull the text straight out with PDF to Text. It is instant and exact.