ddddocr is a Python library for general-purpose offline captcha recognition using optical character recognition.
The tool addresses the problem of identifying various types of captchas locally without external services. It works by training deep neural networks on large batches of randomly generated data to recognize digit-letter combinations, Chinese characters, slider captchas, and special character captchas. The library emphasizes minimal dependencies and simple API design to reduce configuration overhead.
Developers should choose this tool if they need offline, local captcha recognition without relying on external services. It suits projects that process common captcha types including alphanumeric codes, Chinese text, slider verification, and special characters. The README does not compare this tool to alternatives.
The project maintains active development with regular updates to core functionality. Documentation is comprehensive, covering initialization parameters, model selection guidance, performance optimization, and advanced features like batch processing and multi-threading. The maintainers provide troubleshooting guidance for common issues including initialization speed, recognition accuracy, GPU acceleration, and memory usage. The codebase includes support for custom model imports, color filtering, target detection, and multiple preprocessing options for complex captchas.
