Open Access   Article Go Back

Dynamic Vehicle Management System Using Fast Optical Character Recognition Technique

Aditya Rawat1 , Suvigya Awasthi2

Section:Research Paper, Product Type: Journal Paper
Volume-3 , Issue-6 , Page no. 76-81, Jun-2015

Online published on Jun 29, 2015

Copyright © Aditya Rawat , Suvigya Awasthi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Aditya Rawat , Suvigya Awasthi, “Dynamic Vehicle Management System Using Fast Optical Character Recognition Technique,” International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.76-81, 2015.

MLA Style Citation: Aditya Rawat , Suvigya Awasthi "Dynamic Vehicle Management System Using Fast Optical Character Recognition Technique." International Journal of Computer Sciences and Engineering 3.6 (2015): 76-81.

APA Style Citation: Aditya Rawat , Suvigya Awasthi, (2015). Dynamic Vehicle Management System Using Fast Optical Character Recognition Technique. International Journal of Computer Sciences and Engineering, 3(6), 76-81.

BibTex Style Citation:
@article{Rawat_2015,
author = {Aditya Rawat , Suvigya Awasthi},
title = {Dynamic Vehicle Management System Using Fast Optical Character Recognition Technique},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2015},
volume = {3},
Issue = {6},
month = {6},
year = {2015},
issn = {2347-2693},
pages = {76-81},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=554},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=554
TI - Dynamic Vehicle Management System Using Fast Optical Character Recognition Technique
T2 - International Journal of Computer Sciences and Engineering
AU - Aditya Rawat , Suvigya Awasthi
PY - 2015
DA - 2015/06/29
PB - IJCSE, Indore, INDIA
SP - 76-81
IS - 6
VL - 3
SN - 2347-2693
ER -

VIEWS PDF XML
2477 2374 downloads 2377 downloads
  
  
           

Abstract

Vehicles have become one of the most common commodities that the masses have adopted during these modern times. Tracking all such vehicles and regulating their inflow and outflow at any institution/organization is still a labor intensive task. With the development of technology, every day we come across one or the other situation where technology is skillfully replacing all such labor orientated tasks with faultless automated systems which are in most situations cheaper and more efficient. The proposed system in this paper elucidates a similar elegant automation solution for the mentioned situation. This system has the ability to register and deregister a vehicle from a database, identifying it based on its number plate which is fed dynamically to the system. Further this proposed technology makes use of a fast optical character recognition system to map the dynamically obtained characters from the license plate by the system to alphanumeric characters used by standard license plates across the country and eventually registers/deregisters users to automate the tedious gatekeeping process, as we know it today.

Key-Words / Index Term

Vehicle management system; License plate recognition; Digital Image Processing; Optical Character Recognition; Machine vision; Machine Learning; Automation Systems; Vehicle Parking Automation Systems

References

[1] Ahmad and Mohammad, 2009,” Efficient Farsi License Plate Recognition”,Information, Communications and Signal Processing, 2009, ISBN:978-1-4244-4657-5, Pages (1-5),8-10 Dec 2009.
[2] Shaohong Wu, “A Novel Accurately Automatic License Plate Localization Method” ICEES, ISBN: 978-1-4577-0576-2, Pages (155-160),10-12July 2011.
[3] Maini R, Aggarwal H, “Study and comparison of various image edge detection techniques”, International Journal of Image Processing, Volume (03), Issue (01), Pages (1-11), 15 March 2009.
[4] V. Koval, V. Turchenko, V. Kochan, A. Sachenko, G. Markowsky”, Smart License Plate Recognition System Based on Image Processing Using Neural Network”,Pages (123-127)8-10 September 2003, Lviv, Ukraine.
[5] Hao Chen, Jisheng Ren, Huachun Tan, Jianqun Wang, “ A novel method for license plate localization”,Image and Graphics, 2007. ICIG 2007, Pages (604-609)22-24 August 2007.
[6] SerkanOzbay, Ergun Ercelebi, “Automatic vehicle identification by plate recognition”, International Science Index, Volume:1, No.:9, Pages (222-225), 2007.
[7] Yungang Zhang, Changshui Zhang, “A New algorithm for character segmentation of license plate”, Proc. Of IEEE Intelligent Vehicles Symposium, Pages(106-109), 2003
[8] Tran DurDuan, Duong AnhDuc, Tran Le, Hong Du, “Combining Hough transform and contour algorithm for detecting vehicle license plate”, Proc. Of International Symposium Intelligent Multimedia, Video and Speech Processing,2004, Pages(747-750).
[9] M. Sarfraz, M. J. Ahmed, and S. A. Ghazi, “Saudi Arabian license plate recognition system”,Geometric Modeling and Graphics Proceedings, Pages(36–41), 16-18 July 2003.
[10] S. Kranthi, K. Pranathi, and A. Srisaila, “Automatic number plate recognition”,International Journal of Advancements in Technology, Volume:2 No.:3, Pages(408–422), July 2003.
[11] Hegt H A, De La Haye R J, Khan N A, “A high performance license plate recognition system”, Systems, Man, and Cybernetics, 1998 IEEE, Pages (4357-4362), 11-14 October 1998.
[12] Sirithinaphong T, Chamnongthai K, “The recognition of car license plate for automatic parking system”, Signal Processing and Its Applications, 1999. ISSPA'99. Proceedings of the Fifth International Symposium on. IEEE, Pages(455-457), 1999.
[13] SubhashTatale, and AkhilKhare, “Real time anpr for vehicle identification using neural network”, International Journal of Advances in Engineering & Technology, IJAET ISSN: 2231- 1963- 262, Vol. 1, Issue 4, Pages (262-268), September 2007.
[14] T. E. de Campos, B. R. Babu, and M. Varma, “Character recognition in natural images,” in Proceedings of the International Conference on Computer Vision Theory and Applications, Lisbon, Portugal, February 2009.
[15] Y. Pan, X. Hou, and C. Liu, “Text localization in natural scene images based on conditional random field,” in International Conference on Document Analysis and Recognition, 2009.
[16] X. Chen and A. Yuille, “Detecting and reading text in natural scenes,” in Computer Vision and Pattern Recognition, vol. 2, 2004.
[17] J. Yang, K. Yu, Y. Gong, and T. S. Huang, “Linear spatial pyramid matching using sparse coding for image classification.” in Computer Vision and Pattern Recognition, 2009.
[18] K. Kavukcuoglu, P. Sermanet, Y. Boureau, K. Gregor, M. Mathieu, and Y. LeCun, “Learning convolutional feature hierarchies for visual recognition,” in Advances in Neural Information Processing Systems, 2010.
[19] R. Smith. “An overview of the Tesseract OCR Engine.” Proc. 9th Int. Conf. on Document Analysis and Recognition, IEEE, Curitiba, Brazil, Pages (629-633), Sep 2007.
[20] The Tesseract open source OCR engine, http://code.google.com/p/tesseract-ocr.