MicroCharNet: Less is More for License Plate Character Detection
2026-07-13 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors created MicroCharNet, a very small and fast computer model to find characters on license plates, which is important for smart transportation systems. Unlike bigger models that need lots of computing power, MicroCharNet is designed to work well even on devices with limited resources. Their tests showed it performs almost as accurately as bigger models but is much lighter and faster, making it good for real-time use. They also shared their code for others to try.
license plate detectiondeep learninglightweight modelC2f blocksCoordAtt moduleanchor-free detectionUFPR-ALPR datasetreal-time deploymentedge devicesYOLO
Authors
Huy Che, Dinh-Duy Phan, Duc-Lung Vu
Abstract
License plate character detection is a crucial component of intelligent transportation systems, where high accuracy and computational efficiency are required for real-time deployment. Although recent deep learning-based methods have substantially improved detection performance, many high-accuracy models rely on large-scale architectures that incur substantial computational overhead, limiting their applicability to resource-constrained devices. In this paper, we propose MicroCharNet, an ultra-lightweight model specifically designed for license plate character detection. The proposed architecture employs a compact backbone composed of C2f blocks, integrated with CoordAtt module to enhance feature extraction while preserving spatial information. A lightweight C3k2-based neck fuses multi-level features, followed by a single-level anchor-free detection head that enables end-to-end prediction. Experiments conducted on the UFPR-ALPR dataset demonstrate that MicroCharNet achieves competitive detection accuracy with only 0.08M parameters and 0.096 GFLOPs, while outperforming several recent YOLO-based baselines. Hardware-level evaluations further confirm its efficiency for real-time deployment on edge devices. These results indicate that carefully designed ultra-lightweight architectures can effectively balance accuracy and efficiency in license plate character detection. The source code is available at https://github.com/chequanghuy/MicroCharNet.