rapidocr
Awesome OCR Library
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Models are downloaded on first use; requires internet access for initial setup.
- 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 · maintenance · safety
Apache-2.0 (permissive) — 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.
last release 2026-07-21 (24 days) · last repo commit 2026-08-14 · 7,492 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,730,324 downloads/mo, #2,512 on PyPI
Alternatives
Verify before relying
pip install rapidocr
from rapidocr import RapidOCR
engine = RapidOCR()
result = engine("path/to/image.jpg")
print(result)- 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.
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 on this page. Verify before relying on it.
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
rapidocr on PyPI
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.
Models are downloaded on first use; requires internet access for initial setup.
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)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 the current Python release <4,>=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 11 packagespyclipperopencv_pythonnumpysixShapelyPyYAMLPillowtqdmomegaconfrequestscolorlog |
| Maintenance | Actively maintained 24 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 3,730,324 / month, #2,512 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: rapidocr-3.9.2-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “ocr text extraction from images”
- rapidocrRapidOCR extracts text from images using ONNX-based models optimized…
- pytesseractPytesseract wraps Google's Tesseract-OCR engine to extract text from…
- kreuzbergExtracts text, tables, images, and metadata from 91+ file formats…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also rapidocr-onnxruntime · cnocr · paddleocr · cnstd · paddlex · img2table · easyocr · onnxtr · kreuzberg · winrt-Windows.Media.Ocr