AI multimodale

PaddleOCR — OCR and document intelligence

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

Ouvrir le dépôt officiel ↗ Page d'accueil du projet ↗
★ 87,8KÉtoiles
⑂ 11,2KForks
230Problèmes ouverts
PythonLangue
Apache-2.0Licence
Q@project.QualityScoreScore éditorial

Captures d'écran du projet

Capture d’écran de PaddleOCR — OCR and document intelligence Capture d’écran de PaddleOCR — OCR and document intelligence

Vue d’ensemble

PaddlePaddle/PaddleOCR is an open-source project in ToolAI's Multimodal AI category. 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 The page is intended to help technical readers decide whether the project is worth a closer review; it is not an endorsement. The Python implementation and the repository owner PaddlePaddle are recorded from the catalog metadata.

Fonctionnalités clés

  • Official repository maintained by PaddlePaddle; the repository description identifies it as ocr and document intelligence.
  • Primary language recorded for this editorial entry: Python.
  • Repository statistics and links are shown as a dated metadata snapshot, so readers can re-check them before adoption.
  • The official README, releases and issue tracker remain the source of truth for installation and compatibility details.

Prérequis, installation et démarrage rapide

Requirements depend on the current official documentation. Start with the repository's README and pin a reviewed release before production use.

git clone https://github.com/PaddlePaddle/PaddleOCR
cd PaddleOCR

Use the project's documented environment manager, model weights and optional accelerators. Do not copy commands from an unverified mirror; check the release notes and lock compatible dependencies first.

Utilisation

This page is an objective orientation for ocr and document intelligence. Read the official quick-start example, run it in an isolated environment, and record the exact commit or release used. For an agent or model workflow, keep tool permissions narrow, validate external inputs, and measure latency and quality on a representative test set. The repository's README and examples should be consulted for API details that may change.

Compatibilité des modèles et cas d’usage

The repository metadata describes the project scope but does not guarantee compatibility with every model, provider, checkpoint or accelerator. Confirm supported model identifiers, tokenizer versions, hardware requirements and license terms in the official documentation before connecting it to an LLM, agent, MCP server or RAG pipeline.

Licence et notes sur les risques

GitHub metadata reports the repository license as Apache-2.0. Review the repository LICENSE and any separate model, dataset, weight or third-party dependency terms before redistribution or commercial use. A missing or NOASSERTION license is not a grant of permission.

Editorial verification 2026-08-02: official repository URL, owner, description, license field and repository statistics were reviewed. License metadata: Apache-2.0. Re-check README, releases and dependency/model terms before production use.

Publication et maintenance

Snapshot: 86,254 stars, 11,085 forks and 216 open issues; repository creation is not stated and last pushed date is 2026-07-22 when supplied by the catalog. Check the official releases page for the current version rather than treating these numbers as a guarantee of maintenance or quality.