Real Time Implementation of a License Plate Location Recognition System Based on Adaptive Morphology

License plate recognition (LPR) by using morphology has the advantage of resistance to brightness changes; high speed processing, and low complexity. However these approaches are sensitive to the distance of the plate from the camera and imaging angle. Various assumptions reported in other works might be unrealistic and cause major problems in practical experiences. In this paper we considered morphological approaches and improved them by using adaptive techniques to achieve more compatibility with practical applications. We examined the developed system on several car plate image databases with different conditions like different camera distance, and different car views. The average achieved rate of success was 89.95% for car plate location recognition, which is around 6.0% higher than previous related report of morphological methods. We further implemented the system on an FPGA platform.

[1]  Subramaniam Ganesan,et al.  An efficient implementation of the Hough transform for detecting vehicle license plates using DSP'S , 1995, Proceedings Real-Time Technology and Applications Symposium.

[2]  A. Çapar,et al.  License Plate Recognition From Still Images and Video Sequences: A Survey , 2008, IEEE Transactions on Intelligent Transportation Systems.

[3]  Duo Qian Miao,et al.  An Improved Method for Vietnam License Plate Location based on Mathematic Morphology and Measuring Properties of Image Regions , 2011 .

[4]  Hideharu Amano,et al.  A High Speed License Plate Recognition System on an FPGA , 2007, 2007 International Conference on Field Programmable Logic and Applications.

[5]  S. Mohammadreza Kasaei,et al.  A Novel Morphological Method for Detection and Recognition of Vehicle License Plates , 2009 .

[6]  Kyung C. Chae,et al.  Batch arrival queue with N-policy and single vacation , 1995, Comput. Oper. Res..

[7]  H Hasanpour,et al.  DESIGN AND IMPLEMENTATION OF A SOFTWARE SYSTEM FOR DETECTING ORTHOGRAPHICAL OR MORPHOLOGICAL ERRORS IN PERSIAN WORDS , 2007 .

[8]  Mohsen Ashourian,et al.  Real Time Implementation of a License Plate Location Recognition System Based on , 2013 .

[9]  Jianxia Wang,et al.  Research and implementation of license plate location based on histogram division method , 2009, 2009 9th International Conference on Electronic Measurement & Instruments.

[10]  Memariani Fatemeh,et al.  Study on Wicking Measurement in Thin Layer Textiles by Processing Digital Images , 2010 .

[11]  Luciano da Fontoura Costa,et al.  Shape Analysis and Classification: Theory and Practice , 2000 .

[12]  Feng Yang,et al.  Vehicle license plate location based on histogramming and mathematical morphology , 2005, Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05).

[13]  Farhad Faradji,et al.  A Morphological-Based License Plate Location , 2007, 2007 IEEE International Conference on Image Processing.

[14]  M. S. Khalil,et al.  Performance comparison between SVM-based and RBF-based for detection of saudi license plate , 2012, 2012 8th International Conference on Information Science and Digital Content Technology (ICIDT2012).

[15]  Abdul Rahman Ramli,et al.  Car license plate detection method for Malaysian plates-styles by using a web camera , 2010 .

[16]  Fernando Martín,et al.  NEW METHODS FOR AUTOMATIC READING OF VLP's (VEHICLE LICENSE PLATES) , 2002 .

[17]  S. Ramalingam,et al.  High definition licence plate detection algorithm , 2012, 2012 Proceedings of IEEE Southeastcon.

[18]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[19]  Sei-Wang Chen,et al.  Automatic license plate recognition , 2004, IEEE Transactions on Intelligent Transportation Systems.

[20]  Mohsen Ashourian,et al.  FPGA implementation of a channel noise canceller for image transmission , 2010, 2010 6th Iranian Conference on Machine Vision and Image Processing.

[21]  Sek M. Chai,et al.  FPGA implementation of a license plate recognition SoC using automatically generated streaming accelerators , 2006, Proceedings 20th IEEE International Parallel & Distributed Processing Symposium.

[22]  Hiroshi Kawakami,et al.  A novel adaptive morphological approach for degraded character image segmentation , 2005, Pattern Recognit..

[23]  Ioannis Anagnostopoulos,et al.  License Plate Recognition From Still Images and Video Sequences: A Survey , 2008, IEEE Transactions on Intelligent Transportation Systems.

[24]  Bo Li,et al.  A vehicle license plate recognition system based on analysis of maximally stable extremal regions , 2012, Proceedings of 2012 9th IEEE International Conference on Networking, Sensing and Control.

[25]  Xiaojun Zhai,et al.  Improved number plate character segmentation algorithm and its efficient FPGA implementation , 2012, Journal of Real-Time Image Processing.

[26]  Mei Xie,et al.  Multiple Processors License Plate Recognition System for Intelligent Transportation Management , 2008, 2008 Second International Symposium on Intelligent Information Technology Application.

[27]  Chen Rongbao,et al.  License plate location method based on modified HSI model of color image , 2009, 2009 9th International Conference on Electronic Measurement & Instruments.

[28]  Mohsen Ashourian,et al.  An FPGA-Based Implementation of Fixed-Point Standard-LMS Algorithm with Low Resource Utilization and Fast Convergence , 2010 .

[29]  Lin Luo,et al.  An Efficient Method of License Plate Location , 2009, 2009 First International Conference on Information Science and Engineering.

[30]  Emanuele Trucco,et al.  Computer and Robot Vision , 1995 .