---
id: cnocr
version: "2.3.3"
license: Apache 2.0
license_treatment: permissive
maintenance: active
---

# cnocr — Python3 package for Chinese/English OCR, with small pretrained models

License: permissive · Maintenance: active · Downloads: 136.8K/mo

## What it is and what it does

CnOCR is a Python OCR toolkit that detects and recognizes text in images, supporting Chinese (simplified and traditional), English, and digits. It ships with 20+ pre-trained models tuned for different scenarios—scene photos, document scans, single-line text, and pure numbers—so you can use it immediately after installation without training. The package automatically calls its companion text-detection engine (CnSTD) to locate text regions before recognition, making it suitable for both simple layouts (like screenshots) and complex real-world scenes.

The library exposes a simple Python API (CnOcr class) and a command-line interface, plus an optional HTTP server for remote inference. It depends on PyTorch, torchvision, and related ML infrastructure (pytorch-lightning, wandb, torchmetrics), so installation pulls in a substantial ML stack. Model selection is configurable—you can swap detection and recognition models, specify language type for multilingual models, or use a lightweight rule-based detector for fast processing of simple documents.

Use it for:

- Extract text from screenshots, scanned documents, or book pages without training a custom model.
- Build a document digitization pipeline that handles mixed Chinese and English text with automatic language detection.
- Recognize pure-digit sequences (bank card numbers, ID codes) using specialized number-only models for higher accuracy.
- Process vertical or rotated text in images using multilingual PP-OCRv6 models.
- Deploy OCR as a microservice via the built-in HTTP endpoint for batch or real-time inference.
- Train custom OCR models on domain-specific data using the provided training CLI.

## Worth the install?

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

CnOCR recognizes text in images—Chinese (simplified and traditional), English, and digits—using pre-trained deep learning models, with built-in text detection and support for both scene photos and document scans.

Yes, if you need Chinese OCR or multilingual text recognition. CnOCR is actively maintained, permissively licensed, and offers a low-friction installation with no compiled dependencies beyond standard ML libraries. The 20+ pre-trained models cover common scenarios (documents, scenes, numbers, vertical text), so most users can apply it without training. Trade-off: PyTorch and its ecosystem are large downloads; if you need only English OCR or have strict size constraints, lighter alternatives may be preferable.

## Install

pip install cnocr
uv add cnocr
poetry add cnocr

## Installing cnocr

Before you install: Low friction: pure Python wheel with no compiled dependencies beyond its runtime stack. Active maintenance—last commit 2026-07-05, 40 days since release. Requires PyTorch, torchvision, and related ML libraries, which are substantial downloads but standard for deep-learning packages.

License in practice: Apache 2.0 permissive license allows commercial and private use without restriction, modification, or redistribution obligations beyond attribution.

Quickstart:

pip install cnocr[ort-cpu]

from cnocr import CnOcr

ocr = CnOcr()
result = ocr.ocr('./image.jpg')
print(result)

Requires Python 3.8 or higher. First-time PyTorch installation may encounter platform-specific issues (OpenCV, CUDA, etc.) that are common but require manual resolution.

Verify before relying:

- Exact accuracy metrics or benchmarks against competing OCR systems are not provided in the fact sheet.
- Whether the package supports GPU acceleration via CUDA beyond the install-time choice (ort-cpu vs ort-gpu) is unclear.
- Performance characteristics (latency per image, throughput) are not documented in the excerpt.

## Package facts

- License: Apache 2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 136.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

chinese ocr text recognition, optical character recognition python, scene text detection and recognition, document image ocr, chinese character recognition, multilingual ocr engine, text extraction from images, ocr-text-recognition, chinese-nlp, document-processing

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