CNN-GRU based fusion architecture for Bengali license plate recognition with explainable AI

Abstract

Because of recent improvements to Bangladesh's roads and highways, Automatic Number Plate Recognition (ALPR) has become a crucial component. Numerous crimes, including kidnapping, failure to pay tolls, and harassment of women, occur on both public and private transportation. The security forces will be able to locate offenders more quickly with the earlier and more accurate detection of license plates. The authors of this research proposing a deep learning-based fusion model for ALPR that integrates CNN and GRU on the basis of these circumstances. A total of 4753 images from various Bangladeshi roads and highways have been collected for training, validation, and testing purposes. The dataset consists of three classes of data namely Private cars, Public buses, and Trucks where all the images are in RGB format. To get precise and reliable findings, a variety of preprocessing approaches have been applied. After passing the images to the proposed architecture all the necessary parameters have been fine-tuned that causes a lesser amount of trainable parameters and more accuracy. The research demonstrates that the suggested CNN-GRU based fusion architecture, with a 98.97% F1-score, outperforms the leading models. Both static photos and CCTV video material can be used to accomplish ALPR tasks with comparable efficiency. Later, Explainable Artificial Intelligence (XAI) model SHAP has been used in order to interpret the outstanding result with a region of features.