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Kiuyha/paddleocr-onnx

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face
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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 for end-to-end document processing.

Source: PaddlePaddle PP-OCRv5 Collection 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 GroupPathLanguagesAccuracySize
Englishlanguages/english/English85.25%7.5 MB
Latinlanguages/latin/French, German, Spanish, Italian, Portuguese, + 27 more84.7%7.5 MB
East Slaviclanguages/eslav/Russian, Bulgarian, Ukrainian, Belarusian81.6%7.5 MB
Koreanlanguages/korean/Korean88.0%13 MB
Chinese/Japaneselanguages/chinese/Chinese, Japanese-81 MB
Thailanguages/thai/Thai82.68%7.5 MB
Greeklanguages/greek/Greek89.28%7.4 MB

PP-OCRv3 Recognition Models (Legacy)

Language GroupPathLanguagesVersionSize
Devanagarilanguages/hindi/Hindi, Marathi, Nepali, Sanskritv38.6 MB
Arabiclanguages/arabic/Arabic, Urdu, Persian/Farsiv38.6 MB
Tamillanguages/tamil/Tamilv38.6 MB
Telugulanguages/telugu/Teluguv38.6 MB

Detection Models

ModelPathVersionSize
PP-OCRv5 Detectiondetection/v5/det.onnxv584 MB
PP-OCRv3 Detectiondetection/v3/det.onnxv32.3 MB

Note: Use v5 detection with v5 recognition models. Use v3 detection with v3 recognition models.

Preprocessing Models (Optional)

ModelPathPurposeAccuracySize
Document Orientationpreprocessing/doc-orientation/Corrects rotated documents (0°, 90°, 180°, 270°)99.06%6.5 MB
Text Line Orientationpreprocessing/textline-orientation/Corrects upside-down text (0°, 180°)98.85%6.5 MB
Document Unwarpingpreprocessing/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 LanguageModel Path
Englishlanguages/english/
French, German, Spanish, Italian, Portugueselanguages/latin/
Russian, Bulgarian, Ukrainian, Belarusianlanguages/eslav/
Koreanlanguages/korean/
Chinese, Japaneselanguages/chinese/
Thailanguages/thai/
Greeklanguages/greek/
Hindi, Marathi, Nepali, Sanskritlanguages/hindi/ + detection/v3/
Arabic, Urdu, Persian/Farsilanguages/arabic/ + detection/v3/
Tamillanguages/tamil/ + detection/v3/
Telugulanguages/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)

ModelAccuracyDataset
Greek89.28%2,799 images
Korean88.0%5,007 images
English85.25%6,530 images
Latin84.7%3,111 images
Thai82.68%4,261 images
East Slavic81.6%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

Links

License: Apache 2.0