Extract Text from Images, Photos, and Scanned Documents with OCR

Manual data entry is slow, expensive, and prone to human error. A paralegal reviewing 50 pages of scanned legal discovery paperwork cannot afford to retype cont

An optical character recognition tool turns static pixels into actionable data. Instead of spending hours retyping printed text, professionals can extract clean

All operations execute locally in your browser memory; confidential files never leave your device.

Access and process files directly on Android, iOS, Windows, and macOS without installing extra software.

Pattern matching against font glyph matrices

Recurrent neural network stroke recognition

Raw images rarely have uniform lighting. Shadows from smartphones or yellowed paper backgrounds degrade recognition. The engine converts the image to grayscale and applies Otsu’s adaptive thresholding algorithm, calculating a dynamic threshold value across localized pixel neighborhoods to separate dark foreground text from light backgrounds cleanly. It then measures horizontal projection profiles to detect and rotate skewed lines back to a level $0^\circ$ baseline.

- Printed Text: Printed characters adhere to standard typographical font rules with fixed kerning, consistent line heights, and uniform stroke thicknesses. The OCR engine uses template matching and feature extraction (detecting loops, ascenders, descenders, and intersections), achieving 99%+ accuracy on clean, high-resolution scans.

Handwriting features infinite personal variability: slanted cursive, irregular letter connections, inconsistent spacing, and broken strokes. Recognizing handwriting requires deeper recurrent neural networks (RNNs) and transformer models that evaluate entire words contextually rather than matching individual isolated letters.

OCR accuracy correlates directly with pixel density. For standard 10–12 point printed body text, the engine requires a minimum resolution where lowercase letters span at least 20 to 30 pixels in height. Scans captured at 300 DPI (dots per inch) provide optimal character clarity; low-resolution images captured below 150 DPI frequently cause broken character loops (e.g., misreading e as c or rn as m).