Blame for README.md

---
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](https://github.com/vn-anhnth/LiteALPR)

## 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](https://github.com/laroca/RodoSol-ALPR)
- **China**: [CBLPRD Dataset](https://github.com/detectRecog/CBLPRD)
- **Vietnam**: [Kaggle License Plates Dataset](https://www.kaggle.com/datasets/duydieunguyen/licenseplates) 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:

```bibtex
@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}
}