OCR resource center
OCR software for documents, tables, and images
Read the page, recover its structure, and check the result before it reaches a spreadsheet or API.
Optical character recognition turns pixels into text. Document extraction goes further. It decides which text is a supplier name, total, transaction row, table header, or handwritten note, then puts those values into a structure you can review.
This guide explains where classic OCR is enough, where AI OCR helps, and what to check before you trust extracted data. The document-specific guides below cover the layouts that cause the most real-world trouble.
OCR is one stage, not the finished dataset
Text OCR
Produces a text layer or a reading-order transcript. It is useful for search, copying, and making scans accessible, but it may flatten tables and separate labels from their values.
Document OCR
Uses page position, reading order, and layout to keep headings, key-value pairs, paragraphs, and regions connected.
OCR data extraction
Returns named fields, rows, and typed values. This is the useful output when a document must feed Excel, JSON, an accounting system, or an API.
OCR guides by source and layout
Run OCR on scanned PDFs, recover text, fields, and tables, and export reviewable document data to Excel, CSV, or JSON.
Extract supplier details, invoice numbers, dates, taxes, totals, and line items from invoice PDFs and images with review-ready OCR.
Extract merchant, date, tax, tip, total, payment details, and itemized purchases from receipt photos, scans, and PDFs.
Extract account details, statement periods, dates, descriptions, debits, credits, and balances from bank statement PDFs and scans.
Extract text, fields, and tables from PNG, JPG, JPEG, and WebP images while preserving layout and review context.
Convert scanned documents into searchable text, named fields, and tables while keeping page order, layout, and review evidence.
Extract rows and columns from scanned tables, borderless layouts, screenshots, and multi-page PDFs into Excel, CSV, or JSON.
Extract clear handwriting from notes, annotations, forms, receipts, and scanned documents with field-level review and source context.
A practical OCR workflow
- Inspect the source: Check whether the file already has selectable text. Native PDFs can often be parsed directly. Scans and photos need visual recognition.
- Read text and layout: The OCR stage detects characters while the document model groups them into fields, paragraphs, tables, and repeating rows.
- Apply a useful schema: The result is organized around the document instead of returned as one long text block. Dates, amounts, identifiers, and line items stay separate.
- Review uncertain values: Compare low-confidence text, totals, row boundaries, and ambiguous characters with the source before export or automation.
Free OCR tools
Test one supported document and review the extraction before creating an account.
- Free Invoice OCR: Read invoice fields and line items without building a template first.
- PDF to Excel AI: Turn PDF tables and document fields into rows you can check before Excel.
- PDF Table Extractor: Pull real rows and columns out of native PDFs and scanned pages.
- Bank Statement to Excel: Convert statement pages into transaction rows without retyping a month of activity.
- Receipt OCR: Read totals, tax, and line items from receipt photos and scans.
OCR software questions
What is the difference between OCR and AI OCR?
OCR recognizes characters in an image. AI OCR usually combines character recognition with layout and document understanding, so it can return fields and tables instead of a plain transcript.
Can OCR extract data from scanned PDFs?
Yes. Each scanned page is treated as an image. Results depend on resolution, rotation, compression, contrast, handwriting, and whether important text is hidden by stamps or folds.
What should an OCR API return?
That depends on the job. Search needs text and page coordinates. Automation usually needs named fields, typed values, repeating rows, page references, confidence, and a way to inspect the source.
Is OCR output accurate enough to automate without review?
Some clean, repeated layouts can reach straight-through processing after testing. Financial totals, account numbers, handwriting, and unfamiliar layouts should start with human review and explicit validation rules.