monkt
/

paddleocr-onnx

@@ -1,387 +1,474 @@
-# PP-OCRv5 ONNX Models
-Fast and accurate multilingual OCR models from PaddleOCR, converted to ONNX format for easy deployment.
-**Original Models**: [PaddlePaddle PP-OCRv5 Collection](https://huggingface.co/collections/PaddlePaddle/pp-ocrv5-684a5356aef5b4b1d7b85e4b)
-**Converted by**: Community contribution
 **Format**: ONNX (optimized for inference)
 **License**: Apache 2.0
 ---
-## 🎯 What's Inside
-This repository contains **11 production-ready ONNX models**:
-- **1 Detection Model** - Finds text in images (works with all languages)
-- **7 Recognition Models** - Reads text in 39+ languages
-- **3 Preprocessing Models** - Fixes rotated or distorted documents (optional)
-**Total Size**: ~258 MB
-**Languages**: English, French, German, Spanish, Italian, Portuguese, Russian, Ukrainian, Korean, Chinese, Japanese, Thai, Greek, and 25+ more!
 ---
-## 🚀 Quick Start
-### Installation
 ```bash
-pip install rapidocr-onnxruntime
 ```
-That's it! No PaddlePaddle, no CUDA required. Works on CPU out of the box.
-### Basic Usage - English
 ```python
 from rapidocr_onnxruntime import RapidOCR
-# Initialize OCR
-ocr = RapidOCR(
-    det_model_path="detection/PP-OCRv5_server_det.onnx",
-    rec_model_path="english/en_PP-OCRv5_mobile_rec.onnx",
-    rec_keys_path="english/ppocrv5_en_dict.txt"
-)
-# Run OCR
-result, elapsed = ocr("your_image.jpg")
-# Print results
-for line in result:
-    text = line[1][0]  # Extracted text
-    confidence = line[1][1]  # Confidence score
-    print(f"{text} (confidence: {confidence:.2%})")
 ```
-### Other Languages
-Just change the model paths:
-```python
-# French, German, Spanish, Italian, etc. (32 languages)
-ocr = RapidOCR(
-    det_model_path="detection/PP-OCRv5_server_det.onnx",
-    rec_model_path="latin/latin_PP-OCRv5_mobile_rec.onnx",
-    rec_keys_path="latin/ppocrv5_latin_dict.txt"
-)
-# Russian, Bulgarian, Ukrainian, Belarusian
-ocr = RapidOCR(
-    det_model_path="detection/PP-OCRv5_server_det.onnx",
-    rec_model_path="eslav/eslav_PP-OCRv5_mobile_rec.onnx",
-    rec_keys_path="eslav/ppocrv5_eslav_dict.txt"
-)
-# Korean
-ocr = RapidOCR(
-    det_model_path="detection/PP-OCRv5_server_det.onnx",
-    rec_model_path="korean/korean_PP-OCRv5_mobile_rec.onnx",
-    rec_keys_path="korean/ppocrv5_korean_dict.txt"
-)
-# Chinese / Japanese
-ocr = RapidOCR(
-    det_model_path="detection/PP-OCRv5_server_det.onnx",
-    rec_model_path="chinese/PP-OCRv5_server_rec.onnx",
-    rec_keys_path="chinese/ppocrv5_dict.txt"
-)
-# Thai
 ocr = RapidOCR(
-    det_model_path="detection/PP-OCRv5_server_det.onnx",
-    rec_model_path="thai/th_PP-OCRv5_mobile_rec.onnx",
-    rec_keys_path="thai/ppocrv5_th_dict.txt"
 )
-# Greek
-ocr = RapidOCR(
-    det_model_path="detection/PP-OCRv5_server_det.onnx",
-    rec_model_path="greek/el_PP-OCRv5_mobile_rec.onnx",
-    rec_keys_path="greek/ppocrv5_el_dict.txt"
-)
 ```
 ---
-## 📦 Available Models
-### Text Recognition Models
-| Model | Languages | Accuracy | Size | Best For |
-|-------|-----------|----------|------|----------|
-| **english/** | English | 85.25% | 7.5 MB | English documents |
-| **latin/** | French, German, Spanish, Italian, Portuguese, Dutch, Polish, Czech, + 24 more | 84.7% | 7.5 MB | European documents |
-| **eslav/** | Russian, Bulgarian, Ukrainian, Belarusian, English | 81.6% | 7.5 MB | Cyrillic scripts |
-| **korean/** | Korean, English | 88.0% | 13 MB | Korean documents |
-| **chinese/** | Chinese, Japanese, English | - | 81 MB | CJK documents |
-| **thai/** | Thai, English | 82.68% | 7.5 MB | Thai documents |
-| **greek/** | Greek, English | 89.28% | 7.4 MB | Greek documents |
-### Detection Model
-- **detection/** - Universal text detection (84 MB) - Works with all languages
-### Preprocessing Models (Optional)
-Enhance OCR accuracy on challenging documents:
-- **preprocessing/doc-orientation/** - Fixes rotated documents (6.5 MB, 99.06% accuracy)
-- **preprocessing/textline-orientation/** - Fixes upside-down text (6.5 MB, 98.85% accuracy)
-- **preprocessing/doc-unwarping/** - Fixes curved/warped pages (30 MB)
 ---
-## 🌍 Supported Languages (39+)
-### Latin Model (32 languages)
-English • French • German • Spanish • Italian • Portuguese • Dutch • Polish • Czech • Slovak • Croatian • Bosnian • Serbian (Latin) • Slovenian • Danish • Norwegian • Swedish • Icelandic • Estonian • Lithuanian • Hungarian • Albanian • Welsh • Irish • Turkish • Indonesian • Malay • Afrikaans • Swahili • Tagalog • Uzbek • Latin
-### Other Models
-- **English** - English (optimized)
-- **East Slavic** - Russian • Bulgarian • Ukrainian • Belarusian
-- **Korean** - Korean
-- **Chinese/Japanese** - Simplified Chinese • Traditional Chinese • Pinyin • Japanese (Hiragana, Katakana, Kanji)
-- **Thai** - Thai
-- **Greek** - Greek
----
-## 📁 Repository Structure
-```
-.
-├── detection/                    # Text detection (84 MB)
-│   ├── PP-OCRv5_server_det.onnx
-│   └── config.json
-│
-├── english/                      # English (7.5 MB)
-│   ├── en_PP-OCRv5_mobile_rec.onnx
-│   ├── ppocrv5_en_dict.txt
-│   └── config.json
-│
-├── latin/                        # 32 languages (7.5 MB)
-│   ├── latin_PP-OCRv5_mobile_rec.onnx
-│   ├── ppocrv5_latin_dict.txt
-│   └── config.json
-│
-├── eslav/                        # Russian/Ukrainian (7.5 MB)
-│   ├── eslav_PP-OCRv5_mobile_rec.onnx
-│   ├── ppocrv5_eslav_dict.txt
-│   └── config.json
-│
-├── korean/                       # Korean (13 MB)
-│   ├── korean_PP-OCRv5_mobile_rec.onnx
-│   ├── ppocrv5_korean_dict.txt
-│   └── config.json
-│
-├── chinese/                      # Chinese/Japanese (81 MB)
-│   ├── PP-OCRv5_server_rec.onnx
-│   ├── ppocrv5_dict.txt
-│   └── config.json
-│
-├── thai/                         # Thai (7.5 MB)
-│   ├── th_PP-OCRv5_mobile_rec.onnx
-│   ├── ppocrv5_th_dict.txt
-│   └── config.json
-│
-├── greek/                        # Greek (7.4 MB)
-│   ├── el_PP-OCRv5_mobile_rec.onnx
-│   ├── ppocrv5_el_dict.txt
-│   └── config.json
-│
-└── preprocessing/                # Optional (43 MB)
-    ├── doc-orientation/
-    ├── textline-orientation/
-    └── doc-unwarping/
-```
-Each model directory contains:
-- **`.onnx`** - The model file
-- **`.txt`** - Character dictionary
-- **`config.json`** - Model metadata
 ---
-## 💡 Why Use These Models?
-### ✅ Advantages
-1. **ONNX Format** - Fast inference, works on any platform (CPU/GPU)
-2. **No PaddlePaddle Required** - Just install `rapidocr-onnxruntime`
-3. **39+ Languages** - Multilingual support out of the box
-4. **Production Ready** - All models tested and validated
-5. **Complete Package** - Detection + Recognition + Dictionaries included
-6. **Well Documented** - Every model has detailed config and usage info
-### 📊 Performance
-- **Speed**: Fast inference on CPU (~100-300ms per image)
-- **Accuracy**: 30% improvement over PP-OCRv3
-- **Size**: Compact models (7-84 MB each)
----
-## 🛠️ Advanced Usage
-### With GPU Acceleration
-```bash
-pip install onnxruntime-gpu
 ```
-Models will automatically use GPU if available for 10x faster inference.
-### Batch Processing
 ```python
 from rapidocr_onnxruntime import RapidOCR
-import glob
 ocr = RapidOCR(
-    det_model_path="detection/PP-OCRv5_server_det.onnx",
-    rec_model_path="latin/latin_PP-OCRv5_mobile_rec.onnx",
-    rec_keys_path="latin/ppocrv5_latin_dict.txt"
 )
-# Process all images in a folder
-for image_path in glob.glob("documents/*.jpg"):
-    result, elapsed = ocr(image_path)
-    print(f"Processed {image_path} in {elapsed:.2f}s")
-    for line in result:
-        print(f"  {line[1][0]}")
-```
-### With Preprocessing (for rotated/distorted documents)
-```python
-# Enable angle classification for rotated text
 ocr = RapidOCR(
-    det_model_path="detection/PP-OCRv5_server_det.onnx",
-    rec_model_path="english/en_PP-OCRv5_mobile_rec.onnx",
-    rec_keys_path="english/ppocrv5_en_dict.txt",
-    use_angle_cls=True,
-    angle_cls_model_path="preprocessing/textline-orientation/PP-LCNet_x1_0_textline_ori.onnx"
 )
 ```
 ---
-## 📖 Model Details
-### How It Works
-1. **Detection** - Finds all text regions in the image
-2. **Recognition** - Reads text from each region using language-specific model
-3. **Decoding** - Converts model output to text using character dictionary
-### Model Specifications
-- **Framework**: Converted from PaddlePaddle to ONNX
-- **ONNX Opset**: 11
-- **Precision**: FP32
-- **Input**: RGB images (dynamic size)
-- **Output**: Text + confidence scores + bounding boxes
-### Accuracy Benchmarks
-Tested on official PP-OCRv5 datasets:
-- Greek: 89.28%
-- Korean: 88.0%
-- English: 85.25%
-- Latin: 84.7%
-- Thai: 82.68%
-- East Slavic: 81.6%
 ---
-## 🎯 Use Cases
-- **Document Digitization** - Scan and extract text from documents
-- **Multilingual OCR** - Process documents in 39+ languages
-- **Mobile Apps** - Lightweight models perfect for mobile deployment
-- **Batch Processing** - Process thousands of documents efficiently
-- **Real-time OCR** - Fast enough for real-time applications
-- **Custom Pipelines** - Integrate into your existing workflows
 ---
-## 📝 Language Selection Guide
-| Your Document | Use This Model |
-|---------------|----------------|
-| English only | `english/` |
-| French, German, Spanish, Italian, etc. | `latin/` (best choice for European languages) |
-| Russian, Bulgarian, Ukrainian, Belarusian | `eslav/` |
-| Korean | `korean/` |
-| Chinese or Japanese | `chinese/` |
-| Thai | `thai/` |
-| Greek | `greek/` |
-| Mixed European languages | `latin/` (supports 32 languages!) |
-**Pro Tip**: The `latin/` model is the most versatile - it handles 32 different languages!
 ---
-## ❓ FAQ
-**Q: Do I need PaddlePaddle installed?**
-A: No! These are ONNX models. Just install `rapidocr-onnxruntime`.
-**Q: Can I use GPU?**
-A: Yes! Install `onnxruntime-gpu` instead of `onnxruntime`.
-**Q: Which model should I use for French?**
-A: Use the `latin/` model - it supports French and 31 other languages.
-**Q: Are these models free to use?**
-A: Yes! Licensed under Apache 2.0.
-**Q: How accurate are these models?**
-A: Very accurate! PP-OCRv5 has 30% better accuracy than PP-OCRv3.
-**Q: Can I use these commercially?**
-A: Yes! Apache 2.0 license allows commercial use.
 ---
-## 🔗 Links
-- **Original Models**: [PaddlePaddle PP-OCRv5 Collection](https://huggingface.co/collections/PaddlePaddle/pp-ocrv5-684a5356aef5b4b1d7b85e4b)
-- **PaddleOCR GitHub**: [github.com/PaddlePaddle/PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
-- **Documentation**: [PaddleOCR Docs](https://paddlepaddle.github.io/PaddleOCR/)
-- **RapidOCR**: [github.com/RapidAI/RapidOCR](https://github.com/RapidAI/RapidOCR)
-- **ONNX Runtime**: [onnxruntime.ai](https://onnxruntime.ai/)
----
-## 🙏 Credits
-- **Original Models**: [PaddlePaddle Team](https://github.com/PaddlePaddle/PaddleOCR)
-- **Conversion**: Community contribution using [paddle2onnx](https://github.com/PaddlePaddle/Paddle2ONNX)
-- **Based on**: [PP-OCRv5 Official Collection](https://huggingface.co/collections/PaddlePaddle/pp-ocrv5-684a5356aef5b4b1d7b85e4b)
 ---
-## 📄 License
-Apache License 2.0 (inherited from PaddleOCR)
-You are free to:
-- ✅ Use commercially
-- ✅ Modify
-- ✅ Distribute
-- ✅ Use privately
 ---
-## 🐛 Issues & Support
-For issues with:
-- **These ONNX models**: Open an issue in this repository
-- **Original PaddleOCR models**: [PaddleOCR Issues](https://github.com/PaddlePaddle/PaddleOCR/issues)
-- **ONNX Runtime**: [onnxruntime Issues](https://github.com/microsoft/onnxruntime/issues)
 ---

+---
+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