This release is a pre-release and may not be stable for production use.
MareArts ANPR
Automatic Number Plate Recognition — plate detection + OCR for 80+ countries, with GPU acceleration, ARM/Windows support, and Vehicle Intelligence (make, model, color, type, side, plate nation, and server-side OCR — cloud or on-device).
One license covers everything: Python SDK · REST API Server · Mobile App · Road Object Detection
Get your license at marearts.com/products/anpr · Try the live demo · Full docs & examples on GitHub
Install
pip install marearts-anpr
Linux / Windows / macOS · Python 3.10–3.14 · CPU by default (CUDA & DirectML optional).
Python SDK
Detect and read license plates in a few lines of Python.
from marearts_anpr import (
ma_anpr_detector_v16, ma_anpr_ocr_v16, marearts_anpr_from_image_file
)
detector = ma_anpr_detector_v16("640p_fp32", user_name, serial_key, signature)
ocr = ma_anpr_ocr_v16("fp32", "univ", user_name, serial_key, signature)
result = marearts_anpr_from_image_file(detector, ocr, "car.jpg")
print(result)
# {'results': [{'ocr': 'ABC1234', 'ocr_conf': 99, 'ltrb': [...], ...}], ...}
REST API Server
Production-ready server with a web dashboard — zero code deployment.
ma-anpr config # one-time credential setup
ma-anpr server start # http://localhost:8000
curl -X POST http://localhost:8000/api/anpr -F "image=@car.jpg"
20+ endpoints: detect, batch, history, watchlist, alerts, stats, config, model selection, and more.
Docker
docker run -d --gpus all -p 8000:8000 \
-e MAREARTS_ANPR_USERNAME="your@email.com" \
-e MAREARTS_ANPR_SERIAL_KEY="your_serial_key" \
-e MAREARTS_ANPR_SIGNATURE="your_signature" \
marearts-anpr-server:latest
Includes CUDA acceleration, automatic CPU fallback, web dashboard, and all endpoints.
Vehicle Intelligence (make / model / color / …)
Go beyond plate text — identify the vehicle itself: make, model, color, type, vehicle side, plate nation, and server-side OCR.
from marearts_anpr import ma_anpr_mmc
mmc = ma_anpr_mmc(user_name, serial_key, signature)
info = mmc.enrich(image, anpr_results) # run ANPR first, then enrich
print(info)
Two backends, identical output fields:
- Cloud — no local model, results in a single API call.
- On-device (offline) — a compact vision-language model (Qwen3-VL-2B) on your GPU/CPU, with no internet and no quota.
ma-anpr mmc-setup # download on-device model + engine (~1.5 GB)
ma-anpr mmc-backend local # switch enrichment to on-device (or: cloud)
ma-anpr mmc-uninstall --all # cleanly remove the engine + model
Also available on the server via POST /api/anpr/mmc, with backend switching through
GET /api/mmc/status and PUT /api/mmc/backend.
Road Object Detection
Detect persons, vehicles, and 2-wheelers in real time — included in your ANPR license.
from marearts_road_objects import ma_road_object_detector
rod = ma_road_object_detector("640p_fp32", user_name, serial_key, signature)
result = rod.detect("street.jpg")
Supported Regions
80+ countries across 12 regional groups, each with per-country character sets for maximum accuracy.
| Code | Region |
|---|---|
eu |
Europe+ (37 countries: EU + Balkans + Indonesia) |
ru |
Ex-USSR (15 countries) |
asia |
Asia (17 countries) |
na |
North America (USA, Canada, Mexico) |
sa |
South America (Brazil, Argentina) |
af |
Africa (South Africa, Nigeria) |
oc |
Oceania (Australia, New Zealand) |
uk |
United Kingdom |
cn |
China (all provinces) |
kr |
Korea (all plate types) |
jp |
Japan (all prefectures) |
univ |
Universal — all of the above |
Pass a 2-letter country code (e.g. de, au, th) for best accuracy, or a group
code. Unknown codes fall back to univ.
Mobile App
On-device ANPR for iOS and Android — parking, security, fleet management. On-device AI (~100–160 ms), offline capable, team collaboration, webhooks, cloud sync, and Vehicle Intelligence.
No additional license required — the app works as your ANPR license.
Support
| Homepage | marearts.com |
| License | marearts.com/products/anpr |
| Live Demo | live.marearts.com |
| Docs | github.com/marearts/marearts-anpr |
| Contact | hello@marearts.com |
| YouTube | Video Examples |
© 2026 MareArts. All rights reserved.
1 maintainer
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Details for the file marearts_anpr-3.10.0b11-cp310-cp310-macosx_10_9_universal2.whl.
File metadata
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- Upload date:
- Size: 3.6 MB
- Tags: CPython 3.10, macOS 10.9+ universal2 (ARM64, x86-64)
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