Tools · 4 min read
OCR Explained: Getting Accurate Text from Images
Optical character recognition (OCR) turns pictures of text — scans, screenshots, photos of receipts — into text you can copy, search, and edit.
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How OCR works
Modern OCR engines such as Tesseract first clean up the image and find blocks and lines of text. A neural network then reads each line as a sequence of characters, using language models to choose likely words. That is why selecting the correct language improves accuracy so much.
What makes OCR fail
Low resolution, blur, skewed or curved pages, uneven lighting, decorative fonts, and text over images all reduce accuracy. Handwriting is much harder than print. Tables and multi-column layouts can come out in the wrong reading order.
Preparing the image
Scan at 300 DPI, photograph straight-on in even light, and crop to just the text. Converting to greyscale and boosting contrast helps. For screenshots, capture at a larger zoom level instead of upscaling afterwards.
Always proofread
Even good OCR confuses similar characters — 0 and O, 1 and l, rn and m. Check numbers, names, and amounts carefully before relying on extracted text, especially from receipts and invoices.