paddleocr

Train, fine-tune, and export PaddleOCR models for text detection and recognition.

17|3|Updated Apr 3, 2026
One-click install
npx skills add https://github.com/jayll1303/AIEKit --skill paddleocr-jayll1303
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: paddleocr
Source: https://github.com/jayll1303/AIEKit/tree/main/.kiro/skills/paddleocr
Command: npx skills add https://github.com/jayll1303/AIEKit --skill paddleocr-jayll1303

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Provides a complete, actionable workflow to train, fine-tune, export, and run high-quality OCR models with PaddlePaddle's PaddleOCR so teams can convert images and documents into accurate structured text without ad-hoc tooling gaps.

Core Features & Use Cases

  • Dataset preparation and annotation: guidance for detection and recognition label formats, PPOCRLabel usage, and dictionary management for multilingual training.
  • Fine-tuning detection and recognition: config recommendations, pretrained model selection, learning-rate scaling rules, and mixed data strategies for domain adaptation.
  • Export and deployment: instructions to export inference models, run Python/CLI inference, enable high-performance inference, ONNX conversion, and MCP/Triton serving integration.
  • Troubleshooting & best practices: OOM handling, AMP tips, export checklist, and evaluation/monitoring advice for production workflows.

Quick Start

Ask the skill to install PaddlePaddle and PaddleOCR, prepare a PaddleOCR-formatted train set, fine-tune a detection or recognition model on that data, export the inference model, and run a sample inference on one image.

Frequently Asked Questions about paddleocr

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune PaddleOCR for custom document recognition?▼

Fine-tune PaddleOCR by preparing a labeled dataset, selecting pretrained detection or recognition models, modifying YAML training configs, and applying learning-rate scaling rules for multi-GPU domain adaptation.

What's the best way to prepare datasets for PaddleOCR multilingual training?▼

Prepare PaddleOCR datasets by formatting detection and recognition labels, using PPOCRLabel for annotation, and managing dictionaries to support multilingual training requirements.

How can I export a PaddleOCR model to ONNX for high-performance inference?▼

Export PaddleOCR to ONNX by first converting the trained model to inference format, then enabling high-performance inference settings, and finally performing ONNX conversion for deployment.

Does PaddleOCR support document parsing for receipts and forms?▼

PaddleOCR supports document parsing for receipts, IDs, and forms by utilizing PP-StructureV3 to detect and extract structured text from multilingual document images.

Why does PaddleOCR training run out of memory and how do I fix it?▼

Fix PaddleOCR training OOM errors by applying Automatic Mixed Precision (AMP) tips, reducing batch sizes, and following memory handling best practices provided in the troubleshooting guidance.

Can I integrate PaddleOCR inference with MCP or Triton serving?▼

Integrate PaddleOCR inference with MCP or Triton serving by exporting the model to inference format, enabling high-performance inference, and configuring the serving integration for production workflows.