Photograph any book page and extract the text for research, note-taking, or digital archiving. Handles both printed and handwritten annotations.
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DoctorDocs offers 244 free OCR and document tools — all running privately in your browser.
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⭐ Leave a review on G2Optical Character Recognition (OCR) is a technology that converts images of text — such as photographs of printed pages, handwritten notes, or screenshots — into machine-readable digital text. Modern OCR engines use deep learning models called LSTM (Long Short-Term Memory) neural networks. Unlike older template-matching approaches that compare letter shapes to a fixed alphabet, LSTM networks learn contextual patterns across entire words and sentences. This means the engine can correctly read a blurry "rn" as an "m" or a partially obscured "d" based on surrounding words, dramatically improving accuracy on real-world documents.
Most online OCR services upload your image to a remote server for processing. This raises privacy concerns, especially for sensitive documents like medical records, legal contracts, or personal letters. DoctorDocs takes a different approach: our core OCR engine (Tesseract.js) runs entirely inside your web browser using WebAssembly. WebAssembly is a low-level binary format that lets complex C++ code execute at near-native speed directly on your device. When you upload an image, it never leaves your computer — the neural network processes it locally. For advanced handwriting recognition that exceeds what local processing can achieve, we use a secure API cascade with enterprise-grade encryption and zero data retention.
Before text recognition begins, the image goes through several pre-processing steps that significantly affect accuracy. First, the image is converted to grayscale, removing color information that can confuse the neural network. Next, a contrast-stretching algorithm (Otsu's binarization) converts the grayscale image into pure black-and-white. This eliminates shadows, gradients, and uneven lighting. Finally, noise reduction removes small specks and artifacts. These pre-processing steps can improve recognition accuracy by 15-30% on poorly lit or low-resolution images. For best results, take photos in good lighting with the text clearly visible and the page as flat as possible.
Our OCR engine supports over 100 languages using Tesseract's trained data models, with optimized accuracy for Latin-script languages (English, Spanish, French, German, Portuguese, Italian) and strong support for Cyrillic, Greek, Arabic, Hindi, Chinese, Japanese, and Korean scripts. Printed text in good quality images typically achieves 95-99% accuracy. Handwritten text varies more widely — neat print handwriting reaches 85-95%, while cursive handwriting achieves 60-85% depending on clarity. Using our Magic Enhance pre-processing tool before OCR can boost handwriting accuracy by an additional 10-20%.
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