# szad670401/end-to-end-for-chinese-plate-recognition

多标签分类,端到端的中文车牌识别基于mxnet, End-to-End Chinese plate recognition base on mxnet

Repository: https://github.com/szad670401/end-to-end-for-chinese-plate-recognition
Canonical: https://ross.abutalabs.com/products/end-to-end-for-chinese-plate-recognition
Language: Python
License Family: other
Last push: 2019-01-08T09:43:23+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3678, "days_push": 2794, "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 1117, forks 516 (observed 2026-08-28T04:03:38.878187+00:00)

## What it is
An end-to-end Chinese license plate recognition model based on MXnet, using multi-label classification. It was trained on ~500k synthetic rendered plate samples and is the predecessor of the HyperLPR system.

## Use cases
- recognize Chinese license plates from images
- train an end-to-end plate recognition model with MXnet
- generate synthetic license plate training samples
- OCR for vehicle plates without separate character segmentation

## When to choose
- you need a lightweight research baseline for Chinese plate recognition on CPU
- you want to study multi-label end-to-end OCR approaches in MXnet

## When to avoid
- you need a production-grade or maintained plate recognizer (use HyperLPR instead)
- you require a permissive license (no license is provided)
- your stack is not MXnet-based

## Facets
- artifact type: library
- maturity: abandoned
- function: machine-learning, deep-learning, computer-vision, ocr, image-processing
- domain: computer-vision, image-processing, deep-learning
- platform: python, cross-platform
- tags: license-plate-recognition, chinese-plate, mxnet, multi-label-classification, end-to-end-ocr, natural-language-processing

## Member repositories
- szad670401/end-to-end-for-chinese-plate-recognition (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:38.878187+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 2026-08-30T06:41:55.428651+00:00, confidence not recorded.
  - readme: https://github.com/szad670401/end-to-end-for-chinese-plate-recognition (fetched 2026-08-28T04:03:38.878187+00:00, sha 6da1cd335041)
- Data as of 2026-08-30T08:39:29.467469+00:00.