OCR
Document ToolsOptical Character Recognition — technology that extracts machine-readable text from images, scanned documents, and non-searchable PDFs.
OCR converts visual representations of text — photographs, scanned pages, image-based PDFs — into selectable, searchable, and editable text. The underlying technology uses pattern recognition to identify characters from pixel data, then assembles them into text strings. Modern OCR engines handle multiple languages, handwriting recognition, and complex layouts with tables and columns.
Accuracy depends heavily on source quality. Clean, high-contrast scans of printed text achieve near-perfect accuracy. Low-resolution photos, handwritten notes, and documents with complex backgrounds introduce errors that require manual correction. For business documents like invoices, receipts, and contracts, OCR accuracy directly affects downstream automation reliability.
OCR is usually the first step in a chain rather than the end of one: extract the text, then feed it into whatever comes next — a search index, a spreadsheet, or a language model. Because the output is imperfect by nature, budget for a proofreading pass on anything where a wrong digit or a dropped negation changes the meaning.
Real-World Example
A researcher ran OCR on twenty scanned journal pages to get searchable text, then proofread the extracted output against the scans before quoting from it.
Related Terms
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Open the humanizer routeFAQ
What is OCR?
Optical Character Recognition — technology that extracts machine-readable text from images, scanned documents, and non-searchable PDFs.
How is OCR used in practice?
A researcher ran OCR on twenty scanned journal pages to get searchable text, then proofread the extracted output against the scans before quoting from it.
What concepts are related to OCR?
Key related concepts include PDF Merge, PDF Compression, ChatPDF. Understanding these together gives a more complete picture of how OCR fits into the AI landscape.