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")

Download entire language folder

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/*"])

Clone entire repository

bash

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

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

PP-OCRv5 Models (English, Latin, East Asian, etc.)

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"
)

PP-OCRv3 Models (Hindi, Arabic, Tamil, Telugu)

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"
)

Full Pipeline with Preprocessing

Optional preprocessing for rotated/distorted documents

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.

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
Conversion: paddle2onnx
Source: PP-OCRv5 Collection

monkt/paddleocr-onnx

PP-OCR ONNX Models

Overview

Quick Start

Download from HuggingFace

Basic Usage

Available Models

PP-OCRv5 Recognition Models

PP-OCRv3 Recognition Models (Legacy)

Detection Models

Preprocessing Models (Optional)

Language Support

PP-OCRv5 Languages (40+)

PP-OCRv3 Languages (8)

Usage Examples

Full Pipeline with Preprocessing

Repository Structure

Model Selection

Technical Specifications

Detection Model

Recognition Model

Performance

Accuracy (PP-OCRv5)

FAQ

Credits

Links

monkt/
paddleocr-onnx