choose for a TypeScript-native OCR pipeline with vision-model speed; not a Python model.
OCR & Document Extraction using vision models
- 12.3k
- TypeScript
- MIT
document OCR / layout-aware extraction
Chandra OCR converts images and PDFs into structured HTML/Markdown/JSON with layout preservation, handling complex tables, forms, and handwriting.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
choose for a TypeScript-native OCR pipeline with vision-model speed; not a Python model.
OCR & Document Extraction using vision models
choose as an alternative Apache-2.0 OCR model with its own performance tradeoffs.
GLM-OCR: Accurate × Fast × Comprehensive
choose for up to 100+ languages and a mature Apache-2.0 OCR ecosystem.
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.
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
choose for a Rust, RAG-focused document pipeline with built-in chunking (AGPL).
Vision infrastructure to turn complex documents into RAG/LLM-ready data
choose for a JVM-based PDF parser producing AI-ready data.
PDF Parser for AI-ready data. Automate PDF accessibility. Open-source.
choose for a composable Tesseract+LLM pipeline with explicit post-processing control.
Enhances Tesseract OCR output using LLMs (local or API) for error correction, smart chunking, and markdown formatting of scanned PDFs
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
choose for converting scanned books with a simpler, MIT-licensed tool.
PDF craft can convert PDF files into various other formats. This project will focus on processing PDF files of scanned books.
choose only for table extraction from scanned PDFs, not full layout.
A set of tools for extracting tables from PDF files helping to do data mining on (OCR-processed) scanned documents.
choose for lightweight text-based PDF parsing without OCR/vision.
A fast, helpful, and open-source document parser
choose for minimal Go OCR via Tesseract when layout analysis is unnecessary.
Go package for OCR (Optical Character Recognition), by using Tesseract C++ library
choose for ultra-lightweight Chinese OCR on edge devices with tiny models.
超轻量级中文ocr,支持竖排文字识别, 支持ncnn、mnn、tnn推理 ( dbnet(1.8M) + crnn(2.5M) + anglenet(378KB)) 总模型仅4.7M
choose for client-side OCR in browsers with no backend/Python.
Pure Javascript OCR for more than 100 Languages 📖🎉🖥
Does the same job, but ships as an app. Useful to a person, not swappable into a codebase.
choose if you are a Paperless-ngx user wanting AI-assisted document digitalization.
Use LLMs and LLM Vision (OCR) to handle paperless-ngx - Document Digitalization powered by AI
archived Android OCR app, not embeddable in a modern codebase.
Experimental optical character recognition app
desktop screenshot/OCR/translation app for interactive use.
🚀 Screenshots, word marking, OCR, AI, translation software || 截图、划词、文字识别、AI、翻译软件
self-hosted Chinese OCR web service with API for simple deployments.
开源易用的中文离线OCR,识别率媲美大厂,并且提供了易用的web页面及web的接口,方便人类日常工作使用或者其他程序来调用~
cross-platform OCR desktop tool for manual text capture.
树洞 OCR 文字识别(一款跨平台的 OCR 小工具)
standalone extraction API wrapping multiple OCR engines; not a model import.
Document (PDF, Word, PPTX ...) extraction and parse API using state of the art modern OCRs + Ollama supported models. Anonymize documents. Remove PII. Convert any document or picture to structured JSON or Markdown
standalone Rust server/CLI for multi-backend OCR models.
Rust multi‑backend OCR/VLM engine (DeepSeek‑OCR-1/2, PaddleOCR‑VL, DotsOCR) with DSQ quantization and an OpenAI‑compatible server & CLI – run locally without Python.
These projects were analysed and named chandra among their alternatives. The relationship is not symmetric — how chandra rates them is a separate judgement, made when chandra is analysed in its own right.
calls chandra “choose as a deep-learning OCR model alternative with strong layout, table, and handwriting support.”
Contexts Optical Compression
calls chandra “Choose for a comparable OCR model with strong layout parsing for complex tables/forms/handwriting; evaluate on your own benchmarks.”
GLM-OCR: Accurate × Fast × Comprehensive
calls chandra “Choose if you need precise OCR for tables, forms, and handwriting in images; it's a model, not a full PDF-to-Markdown pipeline.”
PDF Parser for AI-ready data. Automate PDF accessibility. Open-source.
calls chandra “layout-aware OCR model; choose for complex tables/forms/handwriting”
PandaOCR - 多功能OCR图文识别+翻译+朗读+弹窗+公式+表格+图床+搜图+二维码
calls chandra “Use when you want a dedicated OCR model for complex layouts rather than LLM-corrected Tesseract.”
Enhances Tesseract OCR output using LLMs (local or API) for error correction, smart chunking, and markdown formatting of scanned PDFs
calls chandra “if you need an OCR model specialized in tables, forms, and handwriting”
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