Recognize and extract handwritten text from images and scanned documents.
A person's handwriting is the one format general OCR cannot simply read, because no two writers form the same letter the same way. This tool takes a photo or scan of handwritten text, finds the writing inside the image, and returns the words as plain text you can copy into any document. The surprise most users hit is accuracy: this is not printed-text OCR. Neat, separated letters come out well; cursive, slant, and faint strokes raise the error rate. Budget time to proofread the result against the original image.
The tool treats the image as a picture, not as text. It first locates the regions that contain writing, then splits them into lines, words, and individual letters while compensating for tilt, shadows, and uneven contrast. Each letter shape is compared against letterforms learned from many writers, and the best-matching words are assembled into the output. The whole process runs locally in your browser; the image never leaves your device.
The output is plain text in reading order, ready to paste into a document or spreadsheet. Formatting does not survive: underlines, headings, and page layout are gone, and a word recognized wrongly looks exactly like a correct one, so nothing in the output marks it as doubtful. For words that matter, compare them against the source. Print-style capitals with clear spacing between letters give the most reliable results.
Because human reading uses context, memory, and tolerance that a recognizer only approximates. The tool matches letter shapes against letterforms learned from many writers, and every match is a probability, not a certainty. Errors appear in the output with nothing flagging them as doubtful, so treat the result as a draft and proofread it against the original image, especially for names and numbers.
No. Everything runs locally in the browser and the image never leaves your device, which also means the tool works offline once the page has loaded. That privacy has a trade-off: recognition happens on your machine rather than through a large cloud service, so the accuracy ceiling is whatever local recognition can achieve.
The behavioral notes were checked against the browser implementation. Standards and primary references below define the relevant format, formula, or platform behavior.
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