AUTOMATICS NUMBER PLATE RECOGNITION USING CONVOLUTION NEURAL NETWORK

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Volume 3 (2), December 2020, Pages 234-249

Siddhartha Roy

Faculty of Computer Science, The Heritage College, Calcutta University, Calcutta, India, This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used for security, safety, and also commercial aspects such as parking control access, and legal steps for the red light violation, highway speed detection, and stolen vehicle detection. The license plate of any vehicle contains a number of numeric characters recognized by the computer. Each country in the world has specific characteristics of the license plate. Due to rapid development in the information system field, the previous manual license plate number writing process in the database is replaced by special intelligent device in a real-time environment. Several approaches and techniques are exploited to achieve better systems accuracy and real-time execution. It is a process of recognizing number plates using Optical Character Recognition (OCR) on images. This paper proposes a deep learning-based approach to detect and identify the Indian number plate automatically. It is based on new computer vision algorithms of both number plate detection and character segmentation. The training needs several images to obtain greater accuracy. Initially, we have developed a training set database by training different segmented characters. Several tests were done by varying the Epoch value to observe the change of accuracy. The accuracy is more than 95% that presents an acceptable value compared to related works, which is quite satisfactory and recognizes the blurred number plate.

Keywords:

ANPR, OCR, CNN, Image Segmentation, Image Recognition, Blurred Image.

DOI: https://doi.org/10.32010/26166127.2020.3.2.234.244

 

 

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