{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"CnOCR recognizes text in images\u2014Chinese (simplified and traditional), English, and digits\u2014using pre-trained deep learning models, with built-in text detection and support for both scene photos and document scans.","skillfed_tags":["ocr-text-recognition","chinese-nlp","document-processing"],"use_cases":["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."],"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\u2014scene photos, document scans, single-line text, and pure numbers\u2014so 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.\n\nThe 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\u2014you 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.","worth_installing":"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."},"id":"cnocr","links":{"html":"https://skillfed.io/packages/cnocr","md":"https://skillfed.io/packages/cnocr.md","pypi":"https://pypi.org/project/cnocr/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-05","license_spdx":null,"license_treatment":"permissive","name":"cnocr","python_support":"supports_current","summary":"Python3 package for Chinese/English OCR, with small pretrained models"},"popularity":{"monthly_downloads":136800,"position":11387,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.3.3"}