# Paddleocr Onnx

URL: https://interfaze.ai/models/monktpaddleocr-onnx

[All models](https://interfaze.ai/models)

Paddleocr Onnx by monkt, a image-to-text model with OCR capabilities. Understand and compare OCR features, benchmarks, and capabilities.

## Comparison

| Feature | Paddleocr Onnx | Interfaze |
| --- | --- | --- |
| Input Modalities | image | image, text, audio, video, document |
| Native OCR | Yes | Yes |
| Long Document Processing | No | Yes |
| Language Support | 48 partial | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | unknown | 1M |
| Tool Calling | No | Tool calling supported + built in browser, code execution and web search |

### OCR Capabilities

| Feature | Paddleocr Onnx | Interfaze |
| --- | --- | --- |
| Text Bounding Boxes | Yes | Yes |
| Confidence Scores | No | Yes |
| Dense Image Processing | No | Yes |
| Low Quality Images | No | Yes |
| Handwritten Text | No | Yes |
| Charts, Tables & Equations | No | Yes |

### Scaling

| Feature | Paddleocr Onnx | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/monkt/paddleocr-onnx)

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

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## Overview

**16 models** covering **48+ languages**:

-   11 PP-OCRv5 models (latest, highest accuracy)
-   5 PP-OCRv3 models (legacy, additional language support)

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## Quick Start

### Download from HuggingFace

```
pip install huggingface_hub rapidocr-onnxruntime
```

```
from huggingface_hub import hf_hub_download


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")


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")
```

```
from huggingface_hub import snapshot_download


snapshot_download("monkt/paddleocr-onnx", allow_patterns=["detection/v5/*", "languages/latin/*"])


snapshot_download("monkt/paddleocr-onnx", allow_patterns=["detection/v3/*", "languages/arabic/*"])
```

```
git clone https://huggingface.co/monkt/paddleocr-onnx
cd paddleocr-onnx
```

### Basic Usage

```
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
```

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## 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 |

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## 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

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## Usage Examples

```
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"
)


ocr = RapidOCR(
    det_model_path="detection/v5/det.onnx",
    rec_model_path="languages/latin/rec.onnx",
    rec_keys_path="languages/latin/dict.txt"
)


ocr = RapidOCR(
    det_model_path="detection/v5/det.onnx",
    rec_model_path="languages/eslav/rec.onnx",
    rec_keys_path="languages/eslav/dict.txt"
)


ocr = RapidOCR(
    det_model_path="detection/v5/det.onnx",
    rec_model_path="languages/korean/rec.onnx",
    rec_keys_path="languages/korean/dict.txt"
)


ocr = RapidOCR(
    det_model_path="detection/v5/det.onnx",
    rec_model_path="languages/chinese/rec.onnx",
    rec_keys_path="languages/chinese/dict.txt"
)


ocr = RapidOCR(
    det_model_path="detection/v5/det.onnx",
    rec_model_path="languages/thai/rec.onnx",
    rec_keys_path="languages/thai/dict.txt"
)


ocr = RapidOCR(
    det_model_path="detection/v5/det.onnx",
    rec_model_path="languages/greek/rec.onnx",
    rec_keys_path="languages/greek/dict.txt"
)
```

```
from rapidocr_onnxruntime import RapidOCR


ocr = RapidOCR(
    det_model_path="detection/v3/det.onnx",
    rec_model_path="languages/hindi/rec.onnx",
    rec_keys_path="languages/hindi/dict.txt"
)


ocr = RapidOCR(
    det_model_path="detection/v3/det.onnx",
    rec_model_path="languages/arabic/rec.onnx",
    rec_keys_path="languages/arabic/dict.txt"
)


ocr = RapidOCR(
    det_model_path="detection/v3/det.onnx",
    rec_model_path="languages/tamil/rec.onnx",
    rec_keys_path="languages/tamil/dict.txt"
)


ocr = RapidOCR(
    det_model_path="detection/v3/det.onnx",
    rec_model_path="languages/telugu/rec.onnx",
    rec_keys_path="languages/telugu/dict.txt"
)
```

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## Full Pipeline with Preprocessing

Preprocessing models improve accuracy on rotated or distorted documents:

```
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",
    # 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.

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## 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/`

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## 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/ |

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## 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

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## 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 |

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## 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.

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## 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)

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## 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

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**License**: Apache 2.0

## Want more deterministic results?

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)