An Efficient Method for Indian Number Plate Recognition
Sonal Tiwari1, Nitin Choudhary2

1Sonal Tiwari, Department of Computer Science and Engineering, Kopal Institute of Science Technology, Bhopal (Madhya Pradesh), India.
2Nitin Choudhary, Department of Computer Science and Engineering, Kopal Institute of Science Technology, Bhopal (Madhya Pradesh), India.

Manuscript received on 10 October 2017 | Revised Manuscript received on 18 October 2017 | Manuscript Published on 30 October 2017 | PP: 60-64 | Volume-7 Issue-1, October 2017 | Retrieval Number: A5186107117/17©BEIESP
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Abstract: Number Plate Recognition (ANPR) became a very important tool in our daily life because of the unlimited increase of cars and transportation systems, which make it impossible to be fully managed and monitored by humans. On rising population and on growing need of the people, there is a big rise of using vehicles for the last decades. This increase in the number of vehicles must control from the perspective of security and management. However, controlling a huge amount of traffic is a major problem to be solved. In order to maintain traffic problem and controlling a crime and various agencies working in the field of Indian license plate recognition system. We found some general problem. Here we mention problem • Rate of recognition low. • Creation of template. • Recognition time is very high. • Standard deviation error of most of the method nearer. Yet, it’s a very challenging problem, due to the diversity of plate formats, different scales, rotations and non-uniform illumination conditions during image acquisition. The objective of this paper is to develop an accurate and automatic number plate recognition system. In this paper we propose a license plate recognition technique for the improvement of the recognition rate and recognition time for recognition of the number and the character of the vehicle license plate. We proposed a new technique of Neural Network for Vehicle license plate recognition. The Neural Network generates less recognition times and improves the recognition time of the license recognition system. Our work shows better performance as compare to the correlation method which is one of the efficient techniques for matching. Therefore, the standard deviation error reduces which comes from the data lost during the pre-processing in the recognition process.
Keywords: Edge Detection, Segmentation, Neural Networks, Correlation Method, Radial Basis Function.

Scope of the Article: Pattern Recognition