🤖 AI Summary
This study addresses the challenges of automatic license plate recognition for Bengali due to its complex character set and irregular layout, which hinder accurate localization and text extraction. The authors propose an end-to-end recognition system that first enhances localization precision through a two-stage adaptive training strategy based on YOLOv8, achieving a localization accuracy of 97.83% and an IoU of 91.3%. Subsequently, they introduce a novel vision-language OCR model that uniquely integrates Vision Transformer with BanglaBERT to enable sequential text generation. This approach demonstrates superior performance with a character error rate of 0.1323 and a word error rate of 0.1068, significantly improving robustness across diverse real-world scenarios and proving effective in complex, unconstrained environments.
📝 Abstract
An Automatic License Plate Recognition (ALPR) system constitutes a crucial element in an intelligent traffic management system. However, the detection of Bangla license plates remains challenging because of the complicated character scheme and uneven layouts. This paper presents a robust Bangla License Plate Recognition system that integrates a deep learning-based object detection model for license plate localization with Optical Character Recognition for text extraction. Multiple object detection architectures, including U-Net and several YOLO (You Only Look Once) variants, are compared for license plate localization. This study proposes a novel two-stage adaptive training strategy built upon the YOLOv8 architecture to improve localization performance. The proposed approach outperforms the established models, achieving an accuracy of 97.83% and an Intersection over Union (IoU) of 91.3%. The text recognition problem is phrased as a sequence generation problem with a VisionEncoderDecoder architecture, with a combination of encoder-decoders evaluated. It was demonstrated that the ViT + BanglaBERT model gives better results at the character level, with a Character Error Rate of 0.1323 and Word Error Rate of 0.1068. The proposed system also shows a consistent performance when tested on an external dataset that has been curated for this study purpose. The dataset offers completely different environment and lighting conditions compared to the training sample, indicating the robustness of the proposed framework. Overall, our proposed system provides a robust and reliable solution for Bangla license plate recognition and performs effectively across diverse real-world scenarios, including variations in lighting, noise, and plate styles. These strengths make it well suited for deployment in intelligent transportation applications such as automated law enforcement and access control.