Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.
- ocr
- chineseocr
- pdf2markdown
- pp-ocr
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Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.
Tesseract Open Source OCR Engine (main repository)
Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
TranslatedAn entry-level project for face, video, and text detection and recognition.
Implementation of Nougat Neural Optical Understanding for Academic Documents
TranslatedA ready-to-go translation ocr tool developed with WPF. A ready-to-use translation and OCR tool developed with WPF
Fast and simple OCR library written in Swift
An open-source document parsing model with SOTA accuracy and speed, using an RSI loop during post-training.
Translated[Captcha Recognition - Training] This project is based on CNN/ResNet/DenseNet+GRU/LSTM+CTC/CrossEntropy to realize verification code identification. This project is only for training the model.
Copy any text on your screen, stop retyping.
1M param OCR model - runs on CPU, 15x faster than tesseract, close to 1B+ param models in accuracy
Fast, accurate OCR with correctness assessment.
Papermerge OSS
WeVisDoc is an end-to-end document parser for page images. Fine-tuned from Qwen3-VL-2B-Instruct and Qwen3-VL-4B-Instruct, it turns a page into structured Markdown, with LaTeX formulas and HTML tables.
Scan the MRZ code of a passport and extract the firstname, lastname, passport number, nationality, date of birth, expiration date and personal numer.
On-device Vision Model - a fully offline, free vision pipeline that gives text-only models (e.g. DeepSeek) image understanding and multilingual OCR through an OpenAI-compatible local API. Only tokens are needed: no API key, no subscription, no cloud cost. Recommended for native speakers of Chinese, English, Japanese and Korean.