Bangla License Plate Recognition Using Convolutional Neural Networks (CNN)
This addresses the problem of poor recognition accuracy in Bangla license plate systems for applications like roadside assistance and parking management, but it is incremental as it applies existing CNN methods to a new domain.
The paper tackled Bangla license plate recognition by implementing a Convolutional Neural Networks (CNN)-based system, achieving better accuracy than traditional methods, and created the first standard database for this task.
In the last few years, the deep learning technique in particular Convolutional Neural Networks (CNNs) is using massively in the field of computer vision and machine learning. This deep learning technique provides state-of-the-art accuracy in different classification, segmentation, and detection tasks on different benchmarks such as MNIST, CIFAR-10, CIFAR-100, Microsoft COCO, and ImageNet. However, there are a lot of research has been conducted for Bangla License plate recognition with traditional machine learning approaches in last decade. None of them are used to deploy a physical system for Bangla License Plate Recognition System (BLPRS) due to their poor recognition accuracy. In this paper, we have implemented CNNs based Bangla license plate recognition system with better accuracy that can be applied for different purposes including roadside assistance, automatic parking lot management system, vehicle license status detection and so on. Along with that, we have also created and released a very first and standard database for BLPRS.