AI Image to Text (OCR)

Text Trapped in an Image — Freed in Seconds

You receive a screenshot of an error message, a photo of a printed form, a scanned invoice, or a slide from a presentation that only exists as a JPG. The text is right there — visually — but you cannot select, copy, or search it.

OCR (Optical Character Recognition) solves this. This tool uses Tesseract.js — a WebAssembly port of one of the most widely deployed OCR engines in history — to recognize and extract printed text from images directly inside your browser. No file upload, no API key, no usage limit. Your image never leaves your device.

How Tesseract Reads Your Image

Tesseract has a 40-year history: developed at HP Labs in the 1980s, maintained by Google from 2006–2018, and now a thriving open-source project. Tesseract.js brings it to the browser via WebAssembly.

The OCR pipeline, step by step:

1. Grayscale conversion: The image is converted to single-channel grayscale to simplify processing — color information is not relevant to character recognition
2. Adaptive thresholding: Each pixel is classified as black or white based on local context. This step is critical — it separates text from background even when lighting is uneven
3. Connected component analysis: Groups of adjacent black pixels are identified as potential character components
4. Baseline detection: Character baselines (the invisible line text sits on) are detected to handle slanted or imperfect scans
5. Glyph segmentation: Individual characters are isolated within each detected word region
6. Pattern classification: Each glyph is compared against statistical shape models trained across thousands of font variants
7. Language model correction: A dictionary and bigram frequency model refines raw character guesses into real words — for example, a visually ambiguous character between rn and m is resolved based on the surrounding word context

Result quality depends heavily on the input image. A 300 DPI scan of a printed document with good contrast will achieve near-perfect accuracy. A blurry phone photo taken at an angle in dim lighting will produce significant errors.

Input Quality vs. Expected Accuracy