| Name | marearts-anpr JSON |
| Version |
3.6.5
JSON |
| download |
| home_page | https://www.marearts.com |
| Summary | MareArts ANPR (Automatic Number Plate Recognition) library |
| upload_time | 2025-11-08 11:18:36 |
| maintainer | None |
| docs_url | None |
| author | MareArts |
| requires_python | <3.13,>=3.9 |
| license | None |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# MareArts ANPR SDK
[](https://badge.fury.io/py/marearts-anpr)
[](https://pypi.org/project/marearts-anpr/)
[](https://pepy.tech/project/marearts-anpr)
[](https://study.marearts.com/p/anpr-lpr-solution.html)
[](http://live.marearts.com)
Automatic Number Plate Recognition (ANPR) SDK for multiple regions with GPU acceleration support.
## Features
- 🌍 **Multi-Region Support**: Korean, Europe+, North America, China, and Universal license plates
- 🔄 **Dynamic Region Switching**: Change regions instantly with `set_region()` without model reload
- ⚡ **GPU Acceleration**: CUDA, DirectML support for real-time processing
- 🎯 **High Accuracy**: Advanced models with regional vocabulary optimization
- 📦 **Batch Processing**: Process multiple plates simultaneously
- 🐳 **Production Ready**: Docker API with smart model caching and multi-architecture support
## Quick Start
### Installation
```bash
# CPU Installation
pip install marearts-anpr
# GPU Installation (CUDA, DirectML)
pip install marearts-anpr[gpu] # NVIDIA CUDA
pip install marearts-anpr[directml] # Windows GPU (AMD/Intel/NVIDIA)
```
📦 [See complete installation guide](docs/installation.md)
### Basic Usage
💡 **Model names**: See [models and benchmarks](docs/models.md) (e.g., `small_640p_fp32`, `small_fp32`)
```python
from marearts_anpr import ma_anpr_detector_v14, ma_anpr_ocr_v14, marearts_anpr_from_image_file
# Initialize detector
detector = ma_anpr_detector_v14(
"small_640p_fp32", # pico_640p_fp32, micro_640p_fp32, small_640p_fp32, medium_640p_fp32, large_640p_fp32
user_name,
serial_key,
signature,
backend="cuda", # cpu, cuda, directml (auto-selected if "auto")
conf_thres=0.25, # Detection confidence threshold (default: 0.25)
iou_thres=0.5 # IoU threshold for NMS (default: 0.5)
)
# Initialize OCR with regional vocabulary
ocr = ma_anpr_ocr_v14(
"small_fp32", # Model: pico_fp32, micro_fp32, small_fp32, medium_fp32, large_fp32
"univ", # Region: kr, eup, na, cn, univ (choose specific region for best accuracy)
user_name,
serial_key,
signature
)
# Process image
result = marearts_anpr_from_image_file(detector, ocr, "image.jpg")
print(result)
# Output: {'results': [{'ocr': 'ABC123', 'ocr_conf': 99, ...}], ...}
```
#### Dynamic Region Switching
Switch regions without reinitialization:
```python
ocr.set_region('eup') # Europe+
ocr.set_region('kr') # Korean
ocr.set_region('na') # North America
```
🔄 [Learn more about dynamic region switching](docs/usage.md#dynamic-region-switching-370)
#### Multiple Input Formats & CLI
**From different image sources:**
```python
import cv2
from PIL import Image
from marearts_anpr import marearts_anpr_from_cv2, marearts_anpr_from_pil
result = marearts_anpr_from_cv2(detector, ocr, cv2.imread("image.jpg"))
result = marearts_anpr_from_pil(detector, ocr, Image.open("image.jpg"))
result = marearts_anpr_from_image_file(detector, ocr, "image.jpg")
```
**CLI commands:**
```bash
ma-anpr image.jpg # Process image
ma-anpr test-api image.jpg # Test API (1000/day limit)
ma-anpr validate # Validate license
```
🔧 [See complete usage examples and CLI reference](docs/usage.md)
## Model Performance
### Detector Performance
| Model Name | F1 Score | Speed CUDA | Speed CPU | Notes |
|-------|----------|------------|-----------|-------|
| pico_640p_fp32 | 93.08% | 68.7 FPS (14.5ms) | - | Fastest, smallest model |
| micro_640p_fp32 | 93.39% | 69.5 FPS (14.4ms) | - | Fast with good accuracy |
| small_640p_fp32 | 92.58% | 69.5 FPS (14.4ms) | - | Balanced performance |
| medium_640p_fp32 | 92.27% | 62.1 FPS (16.1ms) | - | Higher accuracy |
| large_640p_fp32 | 93.77% | 57.4 FPS (17.4ms) | - | 🎯 Highest F1 score |
### OCR Performance
*Average across all regions*
| Model Name | Exact Match | Char Accuracy | FPS | Notes |
|-------|-------------|---------------|-----|-------|
| pico_fp32 | 91.78% | 96.65% | 270 | Fastest, smallest |
| micro_fp32 | 91.86% | 96.50% | 262 | Fast with good accuracy |
| small_fp32 | 91.54% | 96.64% | **300** | ⚡ Fastest inference |
| medium_fp32 | 90.36% | 96.45% | 270 | Balanced performance |
| **large_fp32** | **91.70%** | **96.27%** | 262 | 🎯 Best accuracy |
**Supported Regions**: Korean (`kr`), Europe+ (`eup`), North America (`na`), China (`cn`), Universal (`univ`)
📊 [See detailed benchmarks by region](docs/models.md)
## Regional Support
MareArts ANPR supports license plates from multiple regions with specialized vocabulary optimization:
- 🇰🇷 **Korean (`kr`)** - Korean license plates with Hangul characters (best accuracy: 99.27%)
- 🇪🇺 **Europe+ (`eup`)** - EU countries + Albania, Andorra, Bosnia & Herzegovina, Indonesia, and more
- 🇺🇸 **North America (`na`)** - USA and Canada license plates
- 🇨🇳 **China (`cn`)** - Chinese license plates with province codes
- 🌍 **Universal (`univ`)** - All regions (default, but choose specific region for best accuracy)
💡 **Dynamic Region Switching**: Use `ocr.set_region('kr')` to switch regions instantly without reloading the model, saving ~180 MB per additional region.
🌍 [See complete regional support and character sets](docs/regional-support.md)
## Documentation
- 📦 [Installation Guide](docs/installation.md) - Detailed installation options and requirements
- 🔧 [Usage Examples](docs/usage.md) - Python SDK, CLI usage, dynamic region switching, and environment variables
- 💻 [Example Code](example_code/) - Basic, advanced, and batch processing examples
- 🚀 [Model Versions](docs/models.md) - Available models, benchmarks, and performance metrics
- 🌍 [Regional Support](docs/regional-support.md) - Supported countries and character sets
- 🐳 [Docker Deployment](docs/docker.md) - Container setup, API server, and multi-architecture builds
- 🧪 [Try ANPR](docs/api-testing.md) - Test our ANPR without license (1000 requests/day)
- ❓ [FAQ](docs/faq.md) - Licensing, regions, features, and troubleshooting
## MareArts Ecosystem
Explore our AI toolkit:
- **marearts-anpr** - Automatic Number Plate Recognition ([GitHub](https://github.com/MareArts/MareArts-ANPR))
- **marearts-road-objects** - Road object detection for persons, vehicles, and 2-wheelers ([GitHub](https://github.com/MareArts/MareArts-Road-Objects))
- **marearts-xcolor** - Color extraction and similarity analysis ([GitHub](https://github.com/MareArts/MareArts-Xcolor))
- **marearts-mast** - Real-time panoramic stitching ([GitHub](https://github.com/MareArts/MareArts-MAST))
- **marearts-crystal** - Encryption and decryption toolkit ([PyPI](https://pypi.org/project/marearts-crystal/))
## Support & Resources
| Resource | Link |
|----------|------|
| 📧 **Contact** | [hello@marearts.com](mailto:hello@marearts.com) |
| 🏠 **Homepage** | [https://marearts.com](https://marearts.com) |
| 💳 **License Purchase** | [ANPR Solution](https://study.marearts.com/p/anpr-lpr-solution.html) |
| 🎮 **Live Demo** | [http://live.marearts.com](http://live.marearts.com) |
| 📺 **Video Examples** | [YouTube Playlist](https://www.youtube.com/playlist?list=PLvX6vpRszMkxJBJf4EjQ5VCnmkjfE59-J) |
## License
© 2024 MareArts. All rights reserved.
This software requires a valid license key. Visit [MareArts ANPR Solution](https://study.marearts.com/p/anpr-lpr-solution.html) for licensing options.
Raw data
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"description": "# MareArts ANPR SDK\n\n[](https://badge.fury.io/py/marearts-anpr)\n[](https://pypi.org/project/marearts-anpr/)\n[](https://pepy.tech/project/marearts-anpr)\n[](https://study.marearts.com/p/anpr-lpr-solution.html)\n[](http://live.marearts.com)\n\nAutomatic Number Plate Recognition (ANPR) SDK for multiple regions with GPU acceleration support.\n\n## Features\n\n- \ud83c\udf0d **Multi-Region Support**: Korean, Europe+, North America, China, and Universal license plates\n- \ud83d\udd04 **Dynamic Region Switching**: Change regions instantly with `set_region()` without model reload\n- \u26a1 **GPU Acceleration**: CUDA, DirectML support for real-time processing\n- \ud83c\udfaf **High Accuracy**: Advanced models with regional vocabulary optimization\n- \ud83d\udce6 **Batch Processing**: Process multiple plates simultaneously\n- \ud83d\udc33 **Production Ready**: Docker API with smart model caching and multi-architecture support\n\n## Quick Start\n\n### Installation\n\n```bash\n# CPU Installation\npip install marearts-anpr\n\n# GPU Installation (CUDA, DirectML)\npip install marearts-anpr[gpu] # NVIDIA CUDA\npip install marearts-anpr[directml] # Windows GPU (AMD/Intel/NVIDIA)\n```\n\n\ud83d\udce6 [See complete installation guide](docs/installation.md)\n\n### Basic Usage\n\n\ud83d\udca1 **Model names**: See [models and benchmarks](docs/models.md) (e.g., `small_640p_fp32`, `small_fp32`)\n\n```python\nfrom marearts_anpr import ma_anpr_detector_v14, ma_anpr_ocr_v14, marearts_anpr_from_image_file\n\n# Initialize detector\ndetector = ma_anpr_detector_v14(\n \"small_640p_fp32\", # pico_640p_fp32, micro_640p_fp32, small_640p_fp32, medium_640p_fp32, large_640p_fp32\n user_name,\n serial_key,\n signature,\n backend=\"cuda\", # cpu, cuda, directml (auto-selected if \"auto\")\n conf_thres=0.25, # Detection confidence threshold (default: 0.25)\n iou_thres=0.5 # IoU threshold for NMS (default: 0.5)\n)\n\n# Initialize OCR with regional vocabulary\nocr = ma_anpr_ocr_v14(\n \"small_fp32\", # Model: pico_fp32, micro_fp32, small_fp32, medium_fp32, large_fp32\n \"univ\", # Region: kr, eup, na, cn, univ (choose specific region for best accuracy)\n user_name,\n serial_key,\n signature\n)\n\n# Process image\nresult = marearts_anpr_from_image_file(detector, ocr, \"image.jpg\")\nprint(result)\n# Output: {'results': [{'ocr': 'ABC123', 'ocr_conf': 99, ...}], ...}\n```\n\n#### Dynamic Region Switching\n\nSwitch regions without reinitialization:\n\n```python\nocr.set_region('eup') # Europe+\nocr.set_region('kr') # Korean\nocr.set_region('na') # North America\n```\n\n\ud83d\udd04 [Learn more about dynamic region switching](docs/usage.md#dynamic-region-switching-370)\n\n#### Multiple Input Formats & CLI\n\n**From different image sources:**\n```python\nimport cv2\nfrom PIL import Image\nfrom marearts_anpr import marearts_anpr_from_cv2, marearts_anpr_from_pil\n\nresult = marearts_anpr_from_cv2(detector, ocr, cv2.imread(\"image.jpg\"))\nresult = marearts_anpr_from_pil(detector, ocr, Image.open(\"image.jpg\"))\nresult = marearts_anpr_from_image_file(detector, ocr, \"image.jpg\")\n```\n\n**CLI commands:**\n```bash\nma-anpr image.jpg # Process image\nma-anpr test-api image.jpg # Test API (1000/day limit)\nma-anpr validate # Validate license\n```\n\n\ud83d\udd27 [See complete usage examples and CLI reference](docs/usage.md)\n\n## Model Performance\n\n### Detector Performance\n\n| Model Name | F1 Score | Speed CUDA | Speed CPU | Notes |\n|-------|----------|------------|-----------|-------|\n| pico_640p_fp32 | 93.08% | 68.7 FPS (14.5ms) | - | Fastest, smallest model |\n| micro_640p_fp32 | 93.39% | 69.5 FPS (14.4ms) | - | Fast with good accuracy |\n| small_640p_fp32 | 92.58% | 69.5 FPS (14.4ms) | - | Balanced performance |\n| medium_640p_fp32 | 92.27% | 62.1 FPS (16.1ms) | - | Higher accuracy |\n| large_640p_fp32 | 93.77% | 57.4 FPS (17.4ms) | - | \ud83c\udfaf Highest F1 score |\n\n### OCR Performance\n\n*Average across all regions*\n\n| Model Name | Exact Match | Char Accuracy | FPS | Notes |\n|-------|-------------|---------------|-----|-------|\n| pico_fp32 | 91.78% | 96.65% | 270 | Fastest, smallest |\n| micro_fp32 | 91.86% | 96.50% | 262 | Fast with good accuracy |\n| small_fp32 | 91.54% | 96.64% | **300** | \u26a1 Fastest inference |\n| medium_fp32 | 90.36% | 96.45% | 270 | Balanced performance |\n| **large_fp32** | **91.70%** | **96.27%** | 262 | \ud83c\udfaf Best accuracy |\n\n**Supported Regions**: Korean (`kr`), Europe+ (`eup`), North America (`na`), China (`cn`), Universal (`univ`)\n\n\ud83d\udcca [See detailed benchmarks by region](docs/models.md)\n\n## Regional Support\n\nMareArts ANPR supports license plates from multiple regions with specialized vocabulary optimization:\n\n- \ud83c\uddf0\ud83c\uddf7 **Korean (`kr`)** - Korean license plates with Hangul characters (best accuracy: 99.27%)\n- \ud83c\uddea\ud83c\uddfa **Europe+ (`eup`)** - EU countries + Albania, Andorra, Bosnia & Herzegovina, Indonesia, and more\n- \ud83c\uddfa\ud83c\uddf8 **North America (`na`)** - USA and Canada license plates\n- \ud83c\udde8\ud83c\uddf3 **China (`cn`)** - Chinese license plates with province codes\n- \ud83c\udf0d **Universal (`univ`)** - All regions (default, but choose specific region for best accuracy)\n\n\ud83d\udca1 **Dynamic Region Switching**: Use `ocr.set_region('kr')` to switch regions instantly without reloading the model, saving ~180 MB per additional region.\n\n\ud83c\udf0d [See complete regional support and character sets](docs/regional-support.md)\n\n## Documentation\n\n- \ud83d\udce6 [Installation Guide](docs/installation.md) - Detailed installation options and requirements\n- \ud83d\udd27 [Usage Examples](docs/usage.md) - Python SDK, CLI usage, dynamic region switching, and environment variables\n- \ud83d\udcbb [Example Code](example_code/) - Basic, advanced, and batch processing examples\n- \ud83d\ude80 [Model Versions](docs/models.md) - Available models, benchmarks, and performance metrics\n- \ud83c\udf0d [Regional Support](docs/regional-support.md) - Supported countries and character sets\n- \ud83d\udc33 [Docker Deployment](docs/docker.md) - Container setup, API server, and multi-architecture builds\n- \ud83e\uddea [Try ANPR](docs/api-testing.md) - Test our ANPR without license (1000 requests/day)\n- \u2753 [FAQ](docs/faq.md) - Licensing, regions, features, and troubleshooting\n\n## MareArts Ecosystem\n\nExplore our AI toolkit:\n\n- **marearts-anpr** - Automatic Number Plate Recognition ([GitHub](https://github.com/MareArts/MareArts-ANPR))\n- **marearts-road-objects** - Road object detection for persons, vehicles, and 2-wheelers ([GitHub](https://github.com/MareArts/MareArts-Road-Objects))\n- **marearts-xcolor** - Color extraction and similarity analysis ([GitHub](https://github.com/MareArts/MareArts-Xcolor))\n- **marearts-mast** - Real-time panoramic stitching ([GitHub](https://github.com/MareArts/MareArts-MAST))\n- **marearts-crystal** - Encryption and decryption toolkit ([PyPI](https://pypi.org/project/marearts-crystal/))\n\n## Support & Resources\n\n| Resource | Link |\n|----------|------|\n| \ud83d\udce7 **Contact** | [hello@marearts.com](mailto:hello@marearts.com) |\n| \ud83c\udfe0 **Homepage** | [https://marearts.com](https://marearts.com) |\n| \ud83d\udcb3 **License Purchase** | [ANPR Solution](https://study.marearts.com/p/anpr-lpr-solution.html) |\n| \ud83c\udfae **Live Demo** | [http://live.marearts.com](http://live.marearts.com) |\n| \ud83d\udcfa **Video Examples** | [YouTube Playlist](https://www.youtube.com/playlist?list=PLvX6vpRszMkxJBJf4EjQ5VCnmkjfE59-J) |\n\n## License\n\n\u00a9 2024 MareArts. All rights reserved.\n\nThis software requires a valid license key. Visit [MareArts ANPR Solution](https://study.marearts.com/p/anpr-lpr-solution.html) for licensing options.\n\n\n",
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