AI Image to Text (OCR)

This tool uses Tesseract.js, the JavaScript port of the Tesseract OCR engine originally developed by HP and now maintained by Google. Tesseract uses LSTM (Long Short-Term Memory) neural networks trained on millions of text samples to recognize characters in images. When you upload an image, the Tesseract.js library loads a language-specific trained data file (about 14MB for English) and runs the neural network inference entirely in your browser using WebAssembly. The process analyzes the image for text regions, segments individual characters, and uses the neural network to identify each character with a confidence score. The entire pipeline runs locally — your image never leaves your device.

Image quality is the biggest factor in OCR accuracy. Use high-resolution images (at least 300 DPI for scanned documents). Ensure good contrast between text and background — dark text on a light background works best. Avoid shadows, glare, and skewed angles. For photos of documents, use your phone camera with good lighting and hold it parallel to the document. Crop the image to show only the text area — removing borders, margins, and non-text elements improves accuracy. For multi-language documents, select the primary language. If accuracy is low, try increasing image contrast or brightness before uploading.