LiteALPR / README.md

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license: agpl-3.0
tags:
  - computer-vision
  - object-detection
  - image-to-text
  - yolov8
  - svtr
  - alpr
  - tensorrt
  - automatic-license-plate-recognition
  - automatic-number-plate-recognition
  - license-plate-recognition
  - onnxruntime
  - plate-recognition
  - plate-detection
  - ocr
pipeline_tag: image-to-text

LiteALPR Models

Pretrained weights for LiteALPR, a lightweight open-source library for fast and accurate Automatic License Plate Recognition (ALPR), explicitly optimized for high-throughput execution on both edge and cloud environments.

Model Details

Architecture:
- Detector: YOLOv8n-Efficient (utilizing Ghost Convolution via lightweight C3Ghost blocks).
- Recognizer: SVTR26-Tiny (featuring an Efficient RCTC Decoder to bypass computationally heavy attention mechanisms).
License: GNU Affero General Public License v3.0 (AGPL-3.0)
Repository: LiteALPR GitHub

Training Data

To ensure robust, multi-national generalization, the models were trained and validated on a large-scale aggregated dataset of 52,595 images from diverse geographic regions:

Brazil: RodoSol-ALPR Dataset
China: CBLPRD Dataset
Vietnam: Kaggle License Plates Dataset alongside proprietary self-collected real-world traffic footage.

Half of the recognition crops were synthetically degraded (e.g., motion blur, Gaussian noise, perspective skew) during training to ensure high resilience against challenging real-world scenarios.

Evaluation Results

Detection (YOLOv8n-Efficient): 99.45% mAP@50, 5.69 GFLOPs, 2.00M parameters.
Recognition (SVTR26-Tiny): 89.15% Sequence Accuracy, 3.28% Character Error Rate (CER), 4.22M parameters.
End-to-End Speed: 66.5 FPS on GPU (NVIDIA RTX 3060) and 23.4 FPS on CPU (AMD Ryzen 5 4600G) using ONNX FP32 execution.

Known Limitations

The model currently provides highly accurate out-of-the-box support for single-line alphanumeric license plates (A–Z, Đ, 0–9) typical of many global regions.
Recognition accuracy on heavily curved multi-line plates or non-Latin characters (e.g., Cyrillic, Arabic) may require custom fine-tuning using the provided training scripts.

How to Cite

If you use these models in your research, please cite our paper:

@article{nguyen2026litealpr,
  title={LiteALPR: A lightweight open-source library for fast and accurate license plate recognition},
  author={Nguyen, Anh and Hoang, Vinh Truong},
  journal={SoftwareX},
  year={2026}
}