Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks

• Main Authors: Nur-A-Alam Nur-A-Alam, Mominul Ahsan, Md. Abdul Based, Julfikar Haider
• Format: Article
• Language: English
• Published: MDPI AG 2021-01-01
• Series: Technologies
• Subjects: number plate detection; super resolution technique; convolutional neural networks; deep learning; bounding box method
• Online Access: https://www.mdpi.com/2227-7080/9/1/9

Vehicles on the road are rising in extensive numbers, particularly in proportion to the industrial revolution and growing economy. The significant use of vehicles has increased the probability of traffic rules violation, causing unexpected accidents, and triggering traffic crimes. In order to overcome these problems, an intelligent traffic monitoring system is required. The intelligent system can play a vital role in traffic control through the number plate detection of the vehicles. In this research work, a system is developed for detecting and recognizing of vehicle number plates using a convolutional neural network (CNN), a deep learning technique. This system comprises of two parts: number plate detection and number plate recognition. In the detection part, a vehicle’s image is captured through a digital camera. Then the system segments the number plate region from the image frame. After extracting the number plate region, a super resolution method is applied to convert the low-resolution image into a high-resolution image. The super resolution technique is used with the convolutional layer of CNN to reconstruct the pixel quality of the input image. Each character of the number plate is segmented using a bounding box method. In the recognition part, features are extracted and classified using the CNN technique. The novelty of this research is the development of an intelligent system employing CNN to recognize number plates, which have less resolution, and are written in the Bengali language.