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paddleocr

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PaddleOCR ONNX Models

Summary

This repository provides ONNX-format implementations of PaddleOCR models, offering comprehensive optical character recognition capabilities for multilingual text detection and recognition. The models are exported from the original PaddleOCR framework and optimized for efficient inference across various deployment scenarios.

The repository contains two main types of models: text detection models that identify text regions in images, and text recognition models that convert detected text regions into actual text content. The detection models utilize advanced segmentation-based approaches with post-processing techniques including contour detection, polygon approximation, and score thresholding to accurately localize text regions with high precision.

For text recognition, the models support multiple languages including Chinese, English, Japanese, Korean, Arabic, Cyrillic, Devanagari, and several other scripts. Each recognition model comes with its own character dictionary (dict.txt) tailored to the specific language or script family. The recognition pipeline handles text normalization, feature extraction, and sequence decoding to produce accurate text transcriptions with confidence scores.

The models are designed with practical deployment in mind, offering various versions optimized for different use cases - from lightweight mobile applications to high-accuracy server deployments. Key features include support for rotated text detection, duplicate character removal, and configurable confidence thresholds for balancing precision and recall in real-world applications.

Usage

The models can be easily used through the dghs-imgutils library:

bash

# Installation
pip install dghs-imgutils

python

# Basic OCR usage
from imgutils.ocr import ocr, list_det_models, list_rec_models

# List available models
print("Detection models:", list_det_models())
print("Recognition models:", list_rec_models())

# Perform OCR on an image
results = ocr('your_image.jpg')
for bbox, text, confidence in results:
    print(f"Text: {text}, Confidence: {confidence:.4f}, BBox: {bbox}")

# Custom model selection
results = ocr('your_image.jpg', 
              detect_model='ch_PP-OCRv4_det',
              recognize_model='japan_PP-OCRv3_rec')

python

# Text detection only
from imgutils.ocr import detect_text_with_ocr

# Detect text regions without recognition
detections = detect_text_with_ocr('your_image.jpg')
for bbox, label, confidence in detections:
    print(f"BBox: {bbox}, Confidence: {confidence:.4f}")

Available Models

Text Detection Models

ch_PP-OCRv2_det - Chinese text detection v2
ch_PP-OCRv3_det - Chinese text detection v3
ch_PP-OCRv4_det - Chinese text detection v4
ch_PP-OCRv4_server_det - Server-optimized Chinese detection v4
ch_ppocr_mobile_slim_v2.0_det - Lightweight mobile detection
ch_ppocr_mobile_v2.0_det - Mobile-optimized detection
ch_ppocr_server_v2.0_det - Server-optimized detection
en_PP-OCRv3_det - English text detection

Text Recognition Models

arabic_PP-OCRv3_rec - Arabic text recognition
ch_PP-OCRv2_rec - Chinese text recognition v2
ch_PP-OCRv3_rec - Chinese text recognition v3
ch_PP-OCRv4_rec - Chinese text recognition v4
ch_PP-OCRv4_server_rec - Server-optimized Chinese recognition v4
ch_ppocr_mobile_v2.0_rec - Mobile-optimized Chinese recognition
ch_ppocr_server_v2.0_rec - Server-optimized Chinese recognition
chinese_cht_PP-OCRv3_rec - Traditional Chinese recognition
cyrillic_PP-OCRv3_rec - Cyrillic script recognition
devanagari_PP-OCRv3_rec - Devanagari script recognition
en_PP-OCRv3_rec - English text recognition v3
en_PP-OCRv4_rec - English text recognition v4
en_number_mobile_v2.0_rec - Mobile-optimized number recognition
japan_PP-OCRv3_rec - Japanese text recognition
ka_PP-OCRv3_rec - Kannada text recognition
korean_PP-OCRv3_rec - Korean text recognition
latin_PP-OCRv3_rec - Latin script recognition
ta_PP-OCRv3_rec - Tamil text recognition
te_PP-OCRv3_rec - Telugu text recognition

Model Configuration

The OCR pipeline supports several configurable parameters:

heat_threshold: Heat map threshold for text detection (default: 0.3)
box_threshold: Box confidence threshold (default: 0.7)
max_candidates: Maximum number of text candidates (default: 1000)
unclip_ratio: Expansion ratio for detected boxes (default: 2.0)
rotation_threshold: Aspect ratio threshold for rotation detection (default: 1.5)
is_remove_duplicate: Whether to remove duplicate characters (default: False)

Performance Notes

The default detection model ch_PP-OCRv4_det provides excellent balance of accuracy and speed
The default recognition model ch_PP-OCRv4_rec supports both Chinese and English with high accuracy
For specific languages, choose the corresponding recognition model for optimal results
Server versions generally offer higher accuracy at the cost of increased computational requirements
Mobile versions are optimized for speed and resource efficiency

Original Content

Onnx version of PaddleOCR.

Citation

bibtex

@misc{paddleocr_onnx,
  title        = {{PaddleOCR ONNX Models}},
  author       = {PaddlePaddle and Repository Contributors},
  howpublished = {\url{https://huggingface.co/deepghs/paddleocr}},
  year         = {2023},
  note         = {ONNX-format implementations of PaddleOCR models for multilingual text detection and recognition},
  abstract     = {This repository provides ONNX-format implementations of PaddleOCR models, offering comprehensive optical character recognition capabilities for multilingual text detection and recognition. The models are exported from the original PaddleOCR framework and optimized for efficient inference across various deployment scenarios. The repository contains text detection models that identify text regions in images and text recognition models that convert detected text regions into actual text content, supporting multiple languages including Chinese, English, Japanese, Korean, Arabic, Cyrillic, Devanagari, and several other scripts.},
  keywords     = {OCR, text-detection, text-recognition, multilingual, ONNX}
}

Library-Mutsumi/paddleocr