International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 4, No. 12, 8 Nov. 2012
Cover page and Table of Contents: PDF (size: 791KB)
Implementation of a High Speed Technique for Character Segmentation of License Plate Based on Thresholding Algorithm
Full Text (PDF, 791KB), PP.9-18
Views: 0 Downloads: 0
Author(s)
Bahram Rashidi
1,*
Bahman Rashidi
2
Index Terms
Character Segmentation, Sobel Edge Detection, Thresholding Algorithm, License Plate
Abstract
This paper presents, complete step by step description design and implementation of a high speed technique for character segmentation of license plate based on thresholding algorithm. Because of vertical edges in the plate, fast Sobel edge detection has been used for extracting location of license plate, after stage edge detection the image is segmented by thresholding algorithm and the color of characters is changed to white and the color of background is black. Then, boundary’s pixels of license plate are scanned and their color is changed to black pixels. Afterward the image is scanned vertically and if the number of black pixels in a column is equal to the width of plate or a little few, then the pixels of that column is changed to white pixel, until create white columns between characters, in continue we change pixels around license plate to white pixels. Finally characters are segmented cleanly. We test proposed character segmentation algorithm for stage recognition of number by code that we design. Results of experimentation on different images demonstrate ability of proposed algorithm. The accuracy of proposed character segmentation is 99% and average time of character segmentation is 15ms with thresholding algorithm code and 0.7ms only segmentation character code that is very small in comparison with other algorithms.
Cite This Paper
Bahram Rashidi,Bahman Rashidi,"Implementation of a High Speed Technique for Character Segmentation of License Plate Based on Thresholding Algorithm", IJIGSP, vol.4, no.12, pp.9-18, 2012. DOI: 10.5815/ijigsp.2012.12.02
Reference
[1]Syed Adnan Yusuf, “Identification Of Saudi Arabian License Plates”, a Thesis Master of Science in Information and Computer Science, King Fahd University of Petroleum & Minerals, January, 2005.
[2]Beatriz Díaz Acosta,” Experiments In Image Segmentation for Automatic Us License Plate Recognition”, a Thesis Master of Science in Computer Science Virginia Polytechnic Institute and State University, June 18, 2004.
[3]Deng Hongyao and Song Xiuli , “License Plate Characters Segmentation Using Projection and Template Matching”, IEEE International Conference on Information Technology and Computer Science, pp. 534-537, 2009.
[4]Vojtˇech Franc and V´aclav Hlav´aˇc,” License Plate Character Segmentation Using Hidden Markov Chains”, Springer-Verlag Berlin Heidelberg, pp. 385–392, 2005.
[5]Xiaodan Jia, Xinnian Wang, Wenju Li, Haijiao Wang “A Novel Algorithm for Character Segmentation of Degraded License Plate Based on Prior Knowledge”, This work is supported by doctoral scientific research foundation of Liaoning Province of China Grant ,2006.
[6]Jin Quan, Quan Shuhai, Shi Ying, Xue Zhihua “A Fast License Plate Segmentation and Recognition Method Based on the Modified Template Matching”, IEEE 2nd International Congress Image and Signal Processing , pp.1-6, 2009.
[7]Xianchao Zhang, Xinyue Liu, He Jiang, “A Hybrid Approach to License Plate Segmentation under Complex Conditions”, IEEE Third International Conference Natural Computation, pp. 68-73, 2007.
[8]Baoming shan, “License Plate Character Segmentation and Recognition Based on RBF Neural Network”, IEEE Second International Workshop Education Technology and Computer Science, pp. 86-89, 2010.
[9]José María Lezcano Romero and Abilio Mancuello Petters and Dr. Horacio A. Legal Ayala and Dr. Jacques Facon, “ Moving License Plate Segmentation” , IEEE 17th International Conference on Systems Signals and Image Processing, pp. 256-259, 2010.
[10]Shuang Qiao and Yan Zhu and Xiufen Li and Taihui Liu and Baoxue Zhang, “Research of improving the accuracy of license plate character segmentation”, IEEE Fifth International Conference on Frontier of Computer Science and Technology, China, pp. 489-493, 2010.
[11]Xiangjian He and Lihong Zheng and Qiang Wu and Wenjing Jia and Bijan Samali and Marimuthu Palaniswami, “Segmentation of Characters on Car License Plates”, IEEE 10th Workshop Multimedia Signal Processing , pp.399-402, 2008.
[12]Lei Chao-yang and Liu Jun-hua ,” Vehicle License Plate Character Segmentation Method Based on Watershed Algorithm”, IEEE International Conference Machine Vision and Human-Machine Interface, pp. 447-452, 2010.
[13]C.N. Anagnostopoulos, I. Anagnostopoulos, V. Loumos, and E. Kayafas, “A License Plate-Recognition Algorithm for Intelligent Transportation System Applications”, IEEE Transactions on Intelligent Transportation Systems, Vol (3), pp. 377–392, Sept. 2006.
[14]Yuh-RauWang, “A sliding window technique for efficient license plate localization based on discrete wavelet transform”, Elsevier Expert Systems with Applications, 38(4) pp. 3142-3146, 22 October 2010.
[15]Ioannis Giannoukos, Christos-Nikolaos Anagnostopoulos, Vassili Loumos, Eleftherios Kayafas, “Operator context scanning to support high segmentation rates for real time license plate recognition”, Elsevier Pattern Recognition, 43(11), pp. 3866–3878, 2010.
[16]JIAN-XIA WANG, WAN-ZHEN ZHOU, JING-FU XUE, XIN-XIN LIU, “The research and realization of vehicle license plate character segmentation and recognition technology”, IEEE International Conference on Wavelet Analysis and Pattern Recognition, pp.101–104, 2010.
[17]Abas yaseri, samira torabi, homeira bagheri,” License Plate Recognition (LPR) With Neural Networks”, http://www.4shared.com/zip/Wv5c8p8V/tashkhis-pelak-khodro-worldboo.html.
[18]Bryan S. Morse, “Thresholding”, Brigham Young University, 1998–2000 Last modified on Wednesday, January 12, 2000.
[19]N. Otsu, “A threshold selection method from gray– level histogram,” IEEE Transactions on System Man Cybernatics, Vol. SMC-9, No.1, pp. 62-66, 1979.