For two decades, OCR (Optical Character Recognition) meant Tesseract or a paid SDK trained narrowly on Latin text in clean scans. Anything outside that — handwriting, low-contrast photos, multiple columns, mixed languages — produced gibberish. Multimodal large language models have changed this dramatically.
Why AI-based OCR works better
A multimodal model doesn't just match glyph shapes — it understands context. If a smudged word could be either "invoice" or "involve", the model uses surrounding words ("Invoice #12345 dated…") to pick correctly. It also handles layout reasoning natively: tables, multi-column articles, footnotes, and headers come out in the right reading order.
When it shines
- Photos of documents taken with a phone at an angle, with shadows or reflections.
- Mixed-language pages — French + English in the same paragraph, or scientific notation mixed with prose.
- Handwritten notes — block printing works very well; cursive is hit or miss.
- Tables where traditional OCR loses the column structure.
Tradeoffs
AI OCR is slower per page than Tesseract and costs more in compute. For a clean 200-page typed report, classical OCR is still the right choice. For 20 mixed-quality phone scans, AI wins on both quality and time-to-result.
Try it
Our AI OCR tool uses a multimodal model. You can drop a scanned PDF (or a folder of photos) and get back a searchable PDF with a hidden text layer, plus a plain-text export for grep / spreadsheet workflows. After OCR, you can chat with the result to extract specific data — e.g. "list all dates and amounts mentioned in the document".
Privacy
OCR requires server-side AI processing — there's no way to run a frontier multimodal model in a browser today. We send only the page images needed and delete them from our processing pipeline immediately after returning your result. We never train models on your documents.