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.
Abstract (translated)
自动车牌识别（ALPR）系统是智能交通管理系统中的一个关键组成部分。然而，孟加拉语车牌的检测依然具有挑战性，原因是其复杂的字符结构和不规则布局。本文介绍了一种鲁棒的孟加拉语车牌识别系统，该系统集成了基于深度学习的对象检测模型进行车牌定位以及光学字符识别技术来提取文本内容。针对车牌定位任务，对比了包括U-Net和多个YOLO（You Only Look Once）变体在内的多种对象检测架构。本研究提出了一种新颖的两阶段自适应训练策略，并在其基础上提出了基于YOLOv8架构的方法以提高定位性能。所提方法在精度上达到了97.83%，交并比（IoU）为91.3%，优于现有模型的表现。 文本识别问题被表述为一个序列生成任务，采用视觉编码器-解码器架构进行处理，并对多种编码器和解码器的组合进行了评估。结果显示，ViT + BanglaBERT模型在字符级别上表现更佳，其字符错误率为0.1323，单词错误率为0.1068。 该系统还在一个专门为此研究目的而编写的外部数据集上展示了稳定的性能，该数据集提供了与训练样本完全不同且多变的环境和光照条件，证明了所提出的框架具有较强的鲁棒性。总体而言，我们的提议系统为孟加拉语车牌识别提供了一种稳健可靠的解决方案，并能在包括光线变化、噪声及不同车牌样式在内的各种真实世界场景中有效运行。 这些优势使该系统非常适合部署于智能交通应用中，如自动化执法和访问控制等。
URL
https://arxiv.org/abs/2603.10267
