License Plate Recognition with Feature Salience and Neural Network
Uganya G1, Sudhan M.B2, Shijin Kumar P.S3
1Uganya G, Department of Electronics and Communication Engineering, Saveetha School of Engineering, Chennai, India.
2Sudhan M.B, Department of Electronics and Communication Engineering, VINS Christian College of Engineering, Chunkankadai, Kanyakumari, India.
3Shijin Kumar P.S, Department of Electronics and Communication Engineering, Marri Laxman Reddy Institute of Technology and Mangement, Dundigal, Hyderabad, India.
Manuscript received on 06 August 2019 | Revised Manuscript received on 12 August 2019 | Manuscript published on 30 August 2019 | PP: 2985-2990 | Volume-8 Issue-10, August 2019 | Retrieval Number: J11420881019/2019©BEIESP | DOI: 10.35940/ijitee.J1142.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Character recognition algorithm is considered as a core component of License Plate Recognition (LPR) systems. Numerous methods for License Plate (LP) recognition have been developed in recent years. However, most of them are not advanced enough to recognize in complex background and still demand improvement. This paper introduces a novel system for LPR by analyzing vehicle images. Accurate segmentation of license plate and character extraction from the plate is accomplished. In the plate segmentation module, Hough transform is put forwarded to identify plate edges using line segments. Radon transform adjusts the skew between LP and the viewer, thereby improve the recognition result. Four features are extracted from the LP image, and best features are selected using feature-salience theory. Histogram projection is performed horizontally and vertically to isolate individual characters in the LP. Finally, Back Propagation Neural Network (BPNN) is used to identify the characters present in the LP. From experimental results, it is evident that the proposed system can recognize LP more efficiently and establish a good background for future advancements in LPR.
Index Terms: Hough Transform, Feature Salience, Histogram Projection, Back Propagation Neural Network, License Plate Recognition.
Scope of the Article: Pattern Recognition