chineseocr/chineseocr
Chineseocr
chineseocr is an end-to-end deep learning pipeline for detecting and recognizing Chinese and English text in images. The project combines text region detection using YOLOv3 with sequence-based recognition via Convolutional Recurrent Neural Networks (CRNN) and dense OCR models, forming a complete optical character recognition workflow.
The pipeline includes orientation detection to handle text rotated at 0, 90, 180, or 270 degrees before recognition, and supports structured field extraction from identity cards and train tickets. A multi-framework model converter enables trained models to be converted between darknet, Keras, and PyTorch formats for cross-framework deployment.
The system provides capabilities for text region detection using darknet, OpenCV DNN, or Keras backends, along with variable-length text recognition for both Chinese and English characters. The documentation covers model conversion tools and the end-to-end pipeline configuration.
Features
- Chinese OCR Tools - Provides optical character recognition specifically for Chinese scripts using deep learning models.
- End-to-End Pipelines - Provides an end-to-end pipeline that detects text regions and recognizes Chinese and English characters.
- Text Detectors in Images - Locating and bounding text regions in images containing Chinese and English characters using deep learning models like YOLO3 and darknet.
- CRNN Recognizers - Provides a sequence-based recognizer that reads variable-length Chinese and English text using CRNN models.
- Dense OCR Models - Ships dense OCR models for high-accuracy text extraction from document images.
- YOLO-Based Detectors - Uses YOLOv3 object detection architecture to locate text regions in images with bounding boxes.
- Deep Learning Detectors - Locates text regions in images using darknet, OpenCV DNN, or Keras deep learning models.
- CRNN-Based Recognizers - Provides a CRNN-based recognizer for variable-length Chinese and English text sequences.
- Variable-Length Text Recognizers - Recognize variable-length Chinese and English text from detected regions using CRNN or dense OCR models.
- Cross-Framework Model Conversion - Converts trained models between darknet, Keras, and PyTorch formats for cross-framework deployment.
- Model Format Converters - Converts trained deep learning models between darknet, Keras, and PyTorch formats for cross-framework deployment.
- Model Format Converters - Converts trained text detection and recognition models between darknet, Keras, and PyTorch formats.
- Text Orientation Detection - Detects text orientation at 0, 90, 180, or 270 degrees using deep learning models before recognition.
- Document - Pulls specific fields from identity cards and train tickets using OCR and layout analysis.
- Document Field Extractors - Extracts structured fields from identity cards and train tickets using trained models.
- Document Field Extractors - Extracts structured fields from identity cards and train tickets using trained models.
