# sergiomsilva/alpr-unconstrained

License Plate Detection and Recognition in Unconstrained Scenarios

Repository: https://github.com/sergiomsilva/alpr-unconstrained
Canonical: https://ross.abutalabs.com/products/alpr-unconstrained
Language: C
License: NOASSERTION
License Family: other
Topics: license-plate-recognition, plate-detection, tensorflow, darknet, python, alpr
Last push: 2022-07-01T11:04:20+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2939, "days_push": 1524, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1770, forks 601 (observed 2026-08-28T04:05:33.941028+00:00)

## What it is
An implementation of the ECCV 2018 paper 'License Plate Detection and Recognition in Unconstrained Scenarios', combining a Darknet-based detector with Keras/TensorFlow recognition networks. It detects vehicle license plates in arbitrary images and outputs recognized plate text, with scripts for running inference and retraining the detector.

## Use cases
- read license plates from photos automatically
- detect and recognize car plates in unconstrained images
- build an automatic license plate recognition pipeline
- fine-tune a plate detector on my own annotated dataset
- batch process a folder of vehicle images and export plate text to CSV

## When to choose
- you need research-grade ALPR that works on full unconstrained scenes, not cropped plates
- you want to retrain or fine-tune the plate detector on custom data
- you can run on Linux with Python 2.7-era Keras/TensorFlow and optionally CUDA

## When to avoid
- you need a maintained production system on modern TensorFlow/Python 3
- you only need plate recognition on pre-cropped plate images
- you require a permissive license - the license is non-standard
- you need Windows or macOS support out of the box

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, image-processing, ocr, machine-learning, deep-learning
- domain: computer-vision, image-processing, artificial-intelligence
- platform: python, cpp, c
- tags: alpr, license-plate-recognition, darknet, keras, tensorflow, yolo, eccv-2018, research-code, linux, gpu

## Member repositories
- sergiomsilva/alpr-unconstrained (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:33.941028+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:26:04.253763+00:00, confidence not recorded.
  - readme: https://github.com/sergiomsilva/alpr-unconstrained (fetched 2026-08-28T04:05:33.941028+00:00, sha bff22a2adeec)
- Data as of 2026-08-30T08:39:29.467469+00:00.