DayBreak-u/chineseocr_lite
Chineseocr Lite
chineseocr_lite is a lightweight Chinese optical character recognition engine designed to detect text regions, analyze orientation, and convert Chinese characters from images into digital text. It supports both horizontal and vertical reading layouts and can be deployed as a web service for image uploads and result visualization.
The system utilizes a multi-backend inference framework that supports ncnn, mnn, and tnn, allowing it to run across diverse hardware and platforms. It is specifically engineered for lightweight deployment on mobile and desktop environments through the use of small model files.
The engine implements a pipeline for text orientation analysis and region detection. It also provides a command line interface for processing images and exporting structured data for automated document digitization.
Features
- Chinese Script Recognition - Converts images containing Chinese characters into digital text, supporting both horizontal and vertical layouts.
- Chinese OCR Tools - Provides a complete tool for detecting text regions, analyzing orientation, and converting Chinese image characters to text.
- Chinese Text Recognition - Provides a comprehensive system for converting images containing Chinese characters into digital text.
- Cross-Platform Inference Frameworks - Implements a multi-backend framework supporting ncnn, mnn, and tnn for deployment across diverse hardware and platforms.
- Lightweight OCR Engines - Implements a lightweight OCR engine for Chinese characters using small model files and efficient runtimes.
- Hybrid Convolutional Recurrent Networks - Utilizes a hybrid architecture combining convolutional layers for detection and recurrent networks for sequence recognition.
- Model Quantization - Employs weight quantization to compress the model footprint to under five megabytes for lightweight deployment.
- Inference Backend Switchers - Allows swapping between ncnn, mnn, and tnn runtimes to optimize execution across diverse hardware targets.
- Text Detection - Locates bounding boxes of text within images to isolate characters for recognition.
- Text Recognition - Implements text recognition capabilities including the analysis of text angle and reading order.
- Text Orientation Detection - Implements a model to detect and normalize the rotation angle of text blocks for accurate recognition.
- Lightweight OCR Models - Runs text recognition on low-power hardware using highly compressed model files.
- Multi-Backend Inference Support - Supports running models across multiple backends on CPU and GPU hardware to optimize speed.
- OCR Command Line Interfaces - Provides a command line interface for performing OCR tasks and exporting structured results.
- Cross-Platform Offline OCR - Executes OCR offline across Windows, Linux, macOS, and mobile devices using diverse backends.
- Document Digitization Tools - Automates the extraction of structured text from images for integration into digital workflows.
- Cross-Platform Abstractions - Provides a unified interface to abstract different inference backends across mobile and desktop platforms.
- Pipeline and Processing Architectures - Organizes the OCR process into sequential stages of angle classification, text detection, and character recognition.
Star history
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