PaddlePaddle/PaddleOCR

PaddleOCR – A Production‑Ready OCR & Document‑AI Toolkit

What it does

• Turns images or PDFs into machine‑readable text, tables, formulas and even full‑document structures.
• Outputs are ready for Large Language Models (LLM) – JSON or Markdown that preserve layout information.
• Supports both classic OCR (detect‑and‑recognize text) and advanced vision‑language parsing for complex documents.

Key components

Component	Purpose	Highlights
PP‑OCRv6	General‑purpose scene text spotting	100+ languages in a single model, +4.6 % detection & +5.1 % recognition over v5, 5.2× faster on CPU, tiny (1.5 M) – medium (34.5 M) model sizes for edge‑to‑server deployment
PaddleOCR‑VL‑1.6	Lightweight Vision‑Language Model for document parsing	0.9 B parameters, 96.3 % accuracy on OmniDocBench v1.6, excels at tables, formulas, ancient scripts, seals; outputs Markdown/JSON with element coordinates
PP‑StructureV3	Structured conversion of PDFs/images	Produces fine‑grained layout (cell coordinates, headings, cross‑page tables) and can export to DOCX, Markdown, JSON
HPD‑Parsing (2026‑07‑22)	High‑throughput document parsing	Hierarchical parallel decoding, 4,752 tokens / s, compatible with OpenAI‑style serving or local vLLM runtime

Why it matters for AI/LLM pipelines

• LLM‑ready data: The toolkit gives you clean, structured text plus positional metadata, which is exactly what Retrieval‑Augmented Generation (RAG) or agentic systems need.
• Speed & footprint: Models range from a few megabytes (edge) to ~35 M parameters (server) and run on CPUs, GPUs, XPU, NPU, or via ONNX/TensorRT, making it easy to embed in cloud services or on‑device apps.
• Ecosystem hooks: Pre‑built integrations with popular RAG platforms (Dify, RAGFlow, Pathway, Cherry Studio) and a browser SDK (PaddleOCR.js).

Typical workflow

1. Input – Provide an image, scanned PDF, or a live camera frame.
2. Pipeline selection – Choose a model family:
   • PP‑OCRv6 for fast multilingual text spotting.
   • PP‑StructureV3 for layout‑aware conversion.
   • PaddleOCR‑VL for deep VLM‑based parsing when you need tables, formulas, or ancient scripts.
3. Inference – Run locally (Python, C++, Java, etc.) or via the hosted API. Backend can be Paddle static/dynamic graph, Transformers, ONNX Runtime, OpenVINO, TensorRT, etc.
4. Output – Receive JSON/Markdown with text strings, bounding boxes, table structures, and optional DOCX files.
5. Downstream – Feed the structured output to an LLM for summarisation, Q&A, knowledge‑base construction, or to a RAG engine for retrieval.

Getting started

• Online demo: No install needed – try the Experience Center on https://www.paddleocr.com.
• Local install: pip install paddleocr (Python 3.8‑3.12). Follow the quick‑start links for PP‑OCR, PaddleOCR‑VL, or PP‑StructureV3 documentation.
• Deployment: Convert models to ONNX, then accelerate with OpenVINO, TensorRT, or run multi‑GPU parallel inference. A C++/C#/Java serving layer is also provided.

Who uses it

• Over 6 k GitHub projects depend on PaddleOCR.
• Integrated into commercial RAG platforms (Dify, RAGFlow, Cherry Studio) and AI‑agent frameworks.
• Adopted by enterprises needing high‑accuracy, multilingual document digitisation.

Bottom line PaddleOCR is a mature, open‑source OCR and document‑AI engine that bridges raw visual data to structured, LLM‑friendly formats. Its modular models, multilingual coverage, and flexible deployment options make it a go‑to solution for anyone building AI applications that need to read and understand documents.