---
id: rapidocr
version: "3.9.2"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# rapidocr — Awesome OCR Library

License: permissive · Maintenance: active · Downloads: 3.7M/mo

## What it is and what it does

RapidOCR is an open-source optical character recognition library that converts images into text using ONNX-format models derived from PaddleOCR. It's designed for speed and low resource consumption, supporting offline deployment across multiple platforms and programming languages. The Python package wraps these models with a simple API: instantiate an engine, pass an image (file path or URL), and receive structured text detection and recognition results.

The library depends on 11 runtime packages including opencv_python for image processing, numpy for numerical operations, and Pillow for image handling. It's actively maintained, supports Python 3.8 through 3.13, and has no known security vulnerabilities. The permissive Apache 2.0 license permits commercial use, though the underlying OCR models retain Baidu copyright.

Use it for:

- Extract text from scanned documents or photographs for data entry or archival without cloud dependencies.
- Build document processing pipelines that detect and recognize text in invoices, receipts, or forms.
- Integrate OCR into desktop or embedded applications where offline operation and low latency are required.
- Preprocess images for machine learning workflows that need structured text input from visual data.
- Batch-process large image collections to extract and index text content locally.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

RapidOCR extracts text from images using ONNX-based models optimized for speed and cross-platform deployment, supporting Chinese, English, and other languages with offline inference.

Yes. RapidOCR is worth installing for offline OCR tasks where speed and low resource use matter. It has active maintenance, no security issues, permissive licensing, and low install friction. The 11 dependencies are standard and well-maintained. Primary gotcha: models download on first use and require internet access for initial setup.

## Install

pip install rapidocr
uv add rapidocr
poetry add rapidocr

## Installing rapidocr

Before you install: Low install friction with a pure-Python wheel and 11 well-established runtime dependencies. Active maintenance with a recent release 24 days ago and 7492 repository stars indicate solid ongoing support.

License in practice: Apache 2.0 permissive license allows commercial and private use with minimal restrictions. Note that OCR model copyright is held by Baidu; engineering scripts are owned by the repository.

Quickstart:

pip install rapidocr

from rapidocr import RapidOCR

engine = RapidOCR()
result = engine("path/to/image.jpg")
print(result)

Models are downloaded on first use; requires internet access for initial setup.

Verify before relying:

- Exact model download size and storage requirements on first run.
- Performance benchmarks (speed, accuracy) compared to other OCR libraries.
- Supported languages beyond Chinese and English—documentation reference provided but not detailed here.
- GPU acceleration requirements and setup for different inference backends.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 3.7M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

ocr text extraction from images, optical character recognition python, fast offline ocr, onnx ocr models, text detection and recognition, multilingual ocr, document text extraction, ocr, computer-vision, offline-inference

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