This repository is a curated list of promising OCR resources, targeting researchers and developers in the field of Optical Character Recognition. It aims to provide a comprehensive overview of papers, tools, libraries, and commercial products related to OCR, facilitating easier discovery and adoption of relevant technologies.

How It Works

The project acts as a knowledge aggregator, collecting and categorizing a wide array of OCR-related materials. It covers various aspects of OCR, including deep learning models (CNN, RNN, LSTM, CRNN), specific applications (license plate recognition, receipt scanning, scene text detection), and foundational technologies (Tesseract, PaddleOCR, OcrKing). The list is organized to highlight different approaches and their advantages, such as lightweight models, ONNX backends, and end-to-end trainable systems.

Quick Start & Requirements

This repository is a curated list and does not have a direct installation or execution command. It serves as a reference guide to other projects and resources.

Highlighted Details

• Comprehensive coverage of deep learning techniques like CRNN+CTC and attention mechanisms for OCR.
• Includes both open-source libraries (Tesseract, PaddleOCR, OcrKing) and commercial solutions (ABBYY, IRIS).
• Features resources for specific tasks like scene text recognition, license plate recognition, and document layout analysis.
• Provides links to academic papers and research contributions in the OCR domain.

Maintenance & Community

The project is maintained by wanghaisheng. Community discussion is encouraged via a WeChat QR code provided in the README.

Licensing & Compatibility

The repository itself is a list of links and does not have a specific license. The licenses of the linked projects vary, with many being open-source (e.g., Apache 2.0, MIT) but some commercial products are also listed. Users must refer to the individual project licenses for compatibility and usage terms.

Limitations & Caveats

As a curated list, the quality and maintenance status of the linked resources are not guaranteed by this repository. Some listed projects may be outdated or have limited community support. The README also mentions that Google's open-source projects have a significant performance gap compared to some commercial Chinese OCR solutions.