| --- |
| license: apache-2.0 |
| language: |
| - en |
| - fr |
| - de |
| - es |
| - it |
| - pt |
| - nl |
| - pl |
| - cs |
| - sk |
| - hr |
| - bs |
| - sr |
| - sl |
| - da |
| - "no" |
| - sv |
| - is |
| - et |
| - lt |
| - hu |
| - sq |
| - cy |
| - ga |
| - tr |
| - id |
| - ms |
| - af |
| - sw |
| - tl |
| - uz |
| - la |
| - ru |
| - bg |
| - uk |
| - be |
| - ko |
| - zh |
| - ja |
| - th |
| - el |
| - hi |
| - mr |
| - ne |
| - sa |
| - ar |
| - ur |
| - fa |
| - ta |
| - te |
| tags: |
| - ocr |
| - optical-character-recognition |
| - text-detection |
| - text-recognition |
| - paddleocr |
| - onnx |
| - computer-vision |
| - document-ai |
| library_name: onnx |
| pipeline_tag: image-to-text |
| --- |
| |
| # PP-OCR ONNX Models |
|
|
| Multilingual OCR models from PaddleOCR, converted to ONNX format for production deployment. |
|
|
| **Use as a complete pipeline**: Integrate with [monkt.com](https://monkt.com) for end-to-end document processing. |
|
|
| **Source**: [PaddlePaddle PP-OCRv5 Collection](https://huggingface.co/collections/PaddlePaddle/pp-ocrv5-684a5356aef5b4b1d7b85e4b) |
| **Format**: ONNX (optimized for inference) |
| **License**: Apache 2.0 |
|
|
| --- |
|
|
| ## Overview |
|
|
| **16 models** covering **48+ languages**: |
| - 11 PP-OCRv5 models (latest, highest accuracy) |
| - 5 PP-OCRv3 models (legacy, additional language support) |
|
|
| --- |
|
|
| ## Quick Start |
|
|
| ### Download from HuggingFace |
|
|
| ```bash |
| pip install huggingface_hub rapidocr-onnxruntime |
| ``` |
|
|
| <details> |
| <summary><b>Download specific language models</b></summary> |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| |
| # Download English models |
| det_path = hf_hub_download("monkt/paddleocr-onnx", "detection/v5/det.onnx") |
| rec_path = hf_hub_download("monkt/paddleocr-onnx", "languages/english/rec.onnx") |
| dict_path = hf_hub_download("monkt/paddleocr-onnx", "languages/english/dict.txt") |
| |
| # Use with RapidOCR |
| from rapidocr_onnxruntime import RapidOCR |
| ocr = RapidOCR(det_model_path=det_path, rec_model_path=rec_path, rec_keys_path=dict_path) |
| result, elapsed = ocr("document.jpg") |
| ``` |
|
|
| </details> |
|
|
| <details> |
| <summary><b>Download entire language folder</b></summary> |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| # Download all French/German/Spanish (Latin) models |
| snapshot_download("monkt/paddleocr-onnx", allow_patterns=["detection/v5/*", "languages/latin/*"]) |
| |
| # Download Arabic models (v3) |
| snapshot_download("monkt/paddleocr-onnx", allow_patterns=["detection/v3/*", "languages/arabic/*"]) |
| ``` |
|
|
| </details> |
|
|
| <details> |
| <summary><b>Clone entire repository</b></summary> |
|
|
| ```bash |
| git clone https://huggingface.co/monkt/paddleocr-onnx |
| cd paddleocr-onnx |
| ``` |
|
|
| </details> |
|
|
| ### Basic Usage |
|
|
| ```python |
| from rapidocr_onnxruntime import RapidOCR |
| |
| ocr = RapidOCR( |
| det_model_path="detection/v5/det.onnx", |
| rec_model_path="languages/english/rec.onnx", |
| rec_keys_path="languages/english/dict.txt" |
| ) |
| |
| result, elapsed = ocr("document.jpg") |
| for line in result: |
| print(line[1][0]) # Extracted text |
| ``` |
|
|
| --- |
|
|
| ## Available Models |
|
|
| ### PP-OCRv5 Recognition Models |
|
|
| | Language Group | Path | Languages | Accuracy | Size | |
| |----------------|------|-----------|----------|------| |
| | English | `languages/english/` | English | 85.25% | 7.5 MB | |
| | Latin | `languages/latin/` | French, German, Spanish, Italian, Portuguese, + 27 more | 84.7% | 7.5 MB | |
| | East Slavic | `languages/eslav/` | Russian, Bulgarian, Ukrainian, Belarusian | 81.6% | 7.5 MB | |
| | Korean | `languages/korean/` | Korean | 88.0% | 13 MB | |
| | Chinese/Japanese | `languages/chinese/` | Chinese, Japanese | - | 81 MB | |
| | Thai | `languages/thai/` | Thai | 82.68% | 7.5 MB | |
| | Greek | `languages/greek/` | Greek | 89.28% | 7.4 MB | |
|
|
| ### PP-OCRv3 Recognition Models (Legacy) |
|
|
| | Language Group | Path | Languages | Version | Size | |
| |----------------|------|-----------|---------|------| |
| | Devanagari | `languages/hindi/` | Hindi, Marathi, Nepali, Sanskrit | v3 | 8.6 MB | |
| | Arabic | `languages/arabic/` | Arabic, Urdu, Persian/Farsi | v3 | 8.6 MB | |
| | Tamil | `languages/tamil/` | Tamil | v3 | 8.6 MB | |
| | Telugu | `languages/telugu/` | Telugu | v3 | 8.6 MB | |
|
|
| ### Detection Models |
|
|
| | Model | Path | Version | Size | |
| |-------|------|---------|------| |
| | PP-OCRv5 Detection | `detection/v5/det.onnx` | v5 | 84 MB | |
| | PP-OCRv3 Detection | `detection/v3/det.onnx` | v3 | 2.3 MB | |
|
|
| **Note**: Use v5 detection with v5 recognition models. Use v3 detection with v3 recognition models. |
|
|
| ### Preprocessing Models (Optional) |
|
|
| | Model | Path | Purpose | Accuracy | Size | |
| |-------|------|---------|----------|------| |
| | Document Orientation | `preprocessing/doc-orientation/` | Corrects rotated documents (0°, 90°, 180°, 270°) | 99.06% | 6.5 MB | |
| | Text Line Orientation | `preprocessing/textline-orientation/` | Corrects upside-down text (0°, 180°) | 98.85% | 6.5 MB | |
| | Document Unwarping | `preprocessing/doc-unwarping/` | Fixes curved/warped documents | - | 30 MB | |
|
|
| --- |
|
|
| ## Language Support |
|
|
| ### PP-OCRv5 Languages (40+) |
|
|
| **Latin Script** (32 languages): English, French, German, Spanish, Italian, Portuguese, Dutch, Polish, Czech, Slovak, Croatian, Bosnian, Serbian, Slovenian, Danish, Norwegian, Swedish, Icelandic, Estonian, Lithuanian, Hungarian, Albanian, Welsh, Irish, Turkish, Indonesian, Malay, Afrikaans, Swahili, Tagalog, Uzbek, Latin |
|
|
| **Cyrillic**: Russian, Bulgarian, Ukrainian, Belarusian |
|
|
| **East Asian**: Chinese (Simplified, Traditional), Japanese (Hiragana, Katakana, Kanji), Korean |
|
|
| **Southeast Asian**: Thai |
|
|
| **Other**: Greek |
|
|
| ### PP-OCRv3 Languages (8) |
|
|
| **South Asian**: Hindi, Marathi, Nepali, Sanskrit, Tamil, Telugu |
|
|
| **Middle Eastern**: Arabic, Urdu, Persian/Farsi |
|
|
| --- |
|
|
| ## Usage Examples |
|
|
| <details> |
| <summary><b>PP-OCRv5 Models (English, Latin, East Asian, etc.)</b></summary> |
|
|
| ```python |
| from rapidocr_onnxruntime import RapidOCR |
| |
| # English |
| ocr = RapidOCR( |
| det_model_path="detection/v5/det.onnx", |
| rec_model_path="languages/english/rec.onnx", |
| rec_keys_path="languages/english/dict.txt" |
| ) |
| |
| # French, German, Spanish, etc. (32 languages) |
| ocr = RapidOCR( |
| det_model_path="detection/v5/det.onnx", |
| rec_model_path="languages/latin/rec.onnx", |
| rec_keys_path="languages/latin/dict.txt" |
| ) |
| |
| # Russian, Bulgarian, Ukrainian, Belarusian |
| ocr = RapidOCR( |
| det_model_path="detection/v5/det.onnx", |
| rec_model_path="languages/eslav/rec.onnx", |
| rec_keys_path="languages/eslav/dict.txt" |
| ) |
| |
| # Korean |
| ocr = RapidOCR( |
| det_model_path="detection/v5/det.onnx", |
| rec_model_path="languages/korean/rec.onnx", |
| rec_keys_path="languages/korean/dict.txt" |
| ) |
| |
| # Chinese/Japanese |
| ocr = RapidOCR( |
| det_model_path="detection/v5/det.onnx", |
| rec_model_path="languages/chinese/rec.onnx", |
| rec_keys_path="languages/chinese/dict.txt" |
| ) |
| |
| # Thai |
| ocr = RapidOCR( |
| det_model_path="detection/v5/det.onnx", |
| rec_model_path="languages/thai/rec.onnx", |
| rec_keys_path="languages/thai/dict.txt" |
| ) |
| |
| # Greek |
| ocr = RapidOCR( |
| det_model_path="detection/v5/det.onnx", |
| rec_model_path="languages/greek/rec.onnx", |
| rec_keys_path="languages/greek/dict.txt" |
| ) |
| ``` |
|
|
| </details> |
|
|
| <details> |
| <summary><b>PP-OCRv3 Models (Hindi, Arabic, Tamil, Telugu)</b></summary> |
|
|
| ```python |
| from rapidocr_onnxruntime import RapidOCR |
| |
| # Hindi, Marathi, Nepali, Sanskrit |
| ocr = RapidOCR( |
| det_model_path="detection/v3/det.onnx", |
| rec_model_path="languages/hindi/rec.onnx", |
| rec_keys_path="languages/hindi/dict.txt" |
| ) |
| |
| # Arabic, Urdu, Persian/Farsi |
| ocr = RapidOCR( |
| det_model_path="detection/v3/det.onnx", |
| rec_model_path="languages/arabic/rec.onnx", |
| rec_keys_path="languages/arabic/dict.txt" |
| ) |
| |
| # Tamil |
| ocr = RapidOCR( |
| det_model_path="detection/v3/det.onnx", |
| rec_model_path="languages/tamil/rec.onnx", |
| rec_keys_path="languages/tamil/dict.txt" |
| ) |
| |
| # Telugu |
| ocr = RapidOCR( |
| det_model_path="detection/v3/det.onnx", |
| rec_model_path="languages/telugu/rec.onnx", |
| rec_keys_path="languages/telugu/dict.txt" |
| ) |
| ``` |
|
|
| </details> |
|
|
| --- |
|
|
| ## Full Pipeline with Preprocessing |
|
|
| <details> |
| <summary><b>Optional preprocessing for rotated/distorted documents</b></summary> |
|
|
| Preprocessing models improve accuracy on rotated or distorted documents: |
|
|
| ```python |
| from rapidocr_onnxruntime import RapidOCR |
| |
| # Complete pipeline with preprocessing |
| ocr = RapidOCR( |
| det_model_path="detection/v5/det.onnx", |
| rec_model_path="languages/english/rec.onnx", |
| rec_keys_path="languages/english/dict.txt", |
| # Optional preprocessing |
| use_angle_cls=True, |
| angle_cls_model_path="preprocessing/textline-orientation/PP-LCNet_x1_0_textline_ori.onnx" |
| ) |
| |
| result, elapsed = ocr("rotated_document.jpg") |
| ``` |
|
|
| **When to use preprocessing**: |
| - **Document Orientation** (`doc-orientation/`): Scanned documents with unknown rotation (0°/90°/180°/270°) |
| - **Text Line Orientation** (`textline-orientation/`): Upside-down text lines (0°/180°) |
| - **Document Unwarping** (`doc-unwarping/`): Curved pages, warped documents, camera photos |
|
|
| **Performance impact**: +10-30% accuracy on distorted images, minimal speed overhead. |
|
|
| </details> |
|
|
| --- |
|
|
| ## Repository Structure |
|
|
| ``` |
| . |
| ├── detection/ |
| │ ├── v5/ |
| │ │ ├── det.onnx # 84 MB - PP-OCRv5 detection |
| │ │ └── config.json |
| │ └── v3/ |
| │ ├── det.onnx # 2.3 MB - PP-OCRv3 detection |
| │ └── config.json |
| │ |
| ├── languages/ |
| │ ├── english/ |
| │ │ ├── rec.onnx # 7.5 MB |
| │ │ ├── dict.txt |
| │ │ └── config.json |
| │ ├── latin/ # 32 languages |
| │ ├── eslav/ # Russian, Bulgarian, Ukrainian, Belarusian |
| │ ├── korean/ |
| │ ├── chinese/ # Chinese, Japanese |
| │ ├── thai/ |
| │ ├── greek/ |
| │ ├── hindi/ # Hindi, Marathi, Nepali, Sanskrit (v3) |
| │ ├── arabic/ # Arabic, Urdu, Persian (v3) |
| │ ├── tamil/ # Tamil (v3) |
| │ └── telugu/ # Telugu (v3) |
| │ |
| └── preprocessing/ |
| ├── doc-orientation/ |
| ├── textline-orientation/ |
| └── doc-unwarping/ |
| ``` |
|
|
| --- |
|
|
| ## Model Selection |
|
|
| | Document Language | Model Path | |
| |-------------------|------------| |
| | English | `languages/english/` | |
| | French, German, Spanish, Italian, Portuguese | `languages/latin/` | |
| | Russian, Bulgarian, Ukrainian, Belarusian | `languages/eslav/` | |
| | Korean | `languages/korean/` | |
| | Chinese, Japanese | `languages/chinese/` | |
| | Thai | `languages/thai/` | |
| | Greek | `languages/greek/` | |
| | Hindi, Marathi, Nepali, Sanskrit | `languages/hindi/` + `detection/v3/` | |
| | Arabic, Urdu, Persian/Farsi | `languages/arabic/` + `detection/v3/` | |
| | Tamil | `languages/tamil/` + `detection/v3/` | |
| | Telugu | `languages/telugu/` + `detection/v3/` | |
|
|
| --- |
|
|
| ## Technical Specifications |
|
|
| - **Framework**: PaddleOCR → ONNX |
| - **ONNX Opset**: 11 |
| - **Precision**: FP32 |
| - **Input Format**: RGB images (dynamic size) |
| - **Inference**: CPU/GPU via onnxruntime |
|
|
| ### Detection Model |
| - **Input**: `(batch, 3, height, width)` - dynamic |
| - **Output**: Text bounding boxes |
|
|
| ### Recognition Model |
| - **Input**: `(batch, 3, 32, width)` - height fixed at 32px |
| - **Output**: CTC logits → decoded with dictionary |
|
|
| --- |
|
|
| ## Performance |
|
|
| ### Accuracy (PP-OCRv5) |
|
|
| | Model | Accuracy | Dataset | |
| |-------|----------|---------| |
| | Greek | 89.28% | 2,799 images | |
| | Korean | 88.0% | 5,007 images | |
| | English | 85.25% | 6,530 images | |
| | Latin | 84.7% | 3,111 images | |
| | Thai | 82.68% | 4,261 images | |
| | East Slavic | 81.6% | 7,031 images | |
|
|
| --- |
|
|
| ## FAQ |
|
|
| **Q: Which version should I use?** |
| A: Use PP-OCRv5 models for best accuracy. Use PP-OCRv3 only for South Asian languages not available in v5. |
|
|
| **Q: Can I mix v5 and v3 models?** |
| A: No. Use `detection/v5/det.onnx` with v5 recognition models, and `detection/v3/det.onnx` with v3 recognition models. |
|
|
| **Q: GPU acceleration?** |
| A: Install `onnxruntime-gpu` instead of `onnxruntime` for 10x faster inference. |
|
|
| **Q: Commercial use?** |
| A: Yes. Apache 2.0 license allows commercial use. |
|
|
| --- |
|
|
| ## Credits |
|
|
| - **Original Models**: [PaddlePaddle Team](https://github.com/PaddlePaddle/PaddleOCR) |
| - **Conversion**: [paddle2onnx](https://github.com/PaddlePaddle/Paddle2ONNX) |
| - **Source**: [PP-OCRv5 Collection](https://huggingface.co/collections/PaddlePaddle/pp-ocrv5-684a5356aef5b4b1d7b85e4b) |
|
|
| --- |
|
|
| ## Links |
|
|
| - [PaddleOCR GitHub](https://github.com/PaddlePaddle/PaddleOCR) |
| - [PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/) |
| - [ONNX Runtime](https://onnxruntime.ai/) |
| - [monkt.com](https://monkt.com) - Document processing pipeline |
|
|
| --- |
|
|
| **License**: Apache 2.0 |
