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PaddlePaddle/PaddleOCR
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.
PaddleOCR is Baidu's OCR and document parsing toolkit, covering everything from lightweight text detection/recognition (PP-OCRv6, 1.5M–34.5M params) to full document-to-Markdown/JSON pipelines (PP-StructureV3, PaddleOCR-VL). It's the OCR backbone behind Dify, RAGFlow, and similar RAG stacks. If you need production-grade OCR that runs on-device or on a server without calling a cloud API, this is the serious open-source option.
The three-tier model sizing (tiny/small/medium) is genuinely useful — 1.5M params for edge, 34.5M for server, and the medium tier reportedly beats Qwen3-VL-235B on text recognition benchmarks at a fraction of the compute. The 50-language unified model in PP-OCRv6 is a real engineering win: no model switching for multilingual documents means simpler pipelines. The inference backend flexibility (ONNX, OpenVINO, TensorRT, Transformers) means you're not locked into PaddlePaddle's own runtime, which matters when deploying outside Baidu's ecosystem. Active release cadence — v3.7 shipped June 11, 2026 with measurable accuracy gains — signals this isn't abandonware.
The PaddlePaddle framework dependency is the elephant in the room: most ML engineers live in PyTorch, and while ONNX export exists, the training and fine-tuning path still runs through Paddle. The repo is enormous and the config system is YAML-driven with hundreds of configs — finding the right one for your use case requires reading a lot of docs before anything runs. The VLM path (PaddleOCR-VL) adds a 0.9B model that requires more GPU memory than the name implies for batch workloads. Documentation quality is uneven: Chinese docs are consistently more complete and up-to-date than the English translations.