Improved Automatic License Plate Recognition (ALPR) system based on single pass Connected Component Labeling (CCL) and reign property function

This paper presents improved Automatic License Plate Recognition (ALPR) system based on Single Pass Connected Component Labeling (CCL). This research describes an ALPR system which is capable of distinguishing license plates under various conditions, such as distance from the camera, rotation angle between camera and vehicle (0° to +/-45°) and also poor illumination contrast condition (different weather condition, different lighting condition and physical tilted or damage of license plate). In our method, we apply adaptive thresholding filter to preprocessing step for image enhancement under various conditions, and then to find the location and characters of license plate at the same time we apply improved single pass Connected Component Labeling and regio property function that compared with other methods is fast and accurate. We determine the license plate characters and location according to appropriate size, aspect ratio, distance and connectivity of characters. Finally by using Optical Character Recognition (OCR) we find the characters on each license plate in an image. Image results show the accuracy and reliability of this method.

[1]  Andrew Hunter,et al.  A single-chip FPGA implementation of real-time adaptive background model , 2005, Proceedings. 2005 IEEE International Conference on Field-Programmable Technology, 2005..

[2]  Muhammad Sarfraz,et al.  Saudi Arabian license plate recognition system , 2003, 2003 International Conference on Geometric Modeling and Graphics, 2003. Proceedings.

[3]  Chun-Jen Chen,et al.  A component-labeling algorithm using contour tracing technique , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[4]  Azriel Rosenfeld,et al.  Sequential Operations in Digital Picture Processing , 1966, JACM.

[5]  Priyanto Hidayatullah,et al.  Optical Character Recognition Improvement for License Plate Recognition in Indonesia , 2012, 2012 Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation.

[6]  Ioannis Anagnostopoulos,et al.  A License Plate-Recognition Algorithm for Intelligent Transportation System Applications , 2006, IEEE Transactions on Intelligent Transportation Systems.

[7]  Mehran Rasooli Farsi License Plate Detection based on Element Analysis and Characters Recognition , 2011 .

[8]  Arkadiusz Pawlik High performance automatic number plate recognition in video streams , 2012, 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA).

[9]  P.S. Girao,et al.  Vehicle Plate Recognition for Wireless Traffic Control and Law Enforcement System , 2006, 2006 IEEE International Conference on Industrial Technology.

[10]  Donald G. Bailey,et al.  Optimised single pass connected components analysis , 2008, 2008 International Conference on Field-Programmable Technology.

[11]  Der-Chyuan Lou,et al.  Parallel Execution of a Connected Component Labeling Operation on a Linear Array Architecture , 2003, J. Inf. Sci. Eng..

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

[13]  Ching-Tang Hsieh,et al.  Multiple license plate detection for complex background , 2005, 19th International Conference on Advanced Information Networking and Applications (AINA'05) Volume 1 (AINA papers).

[14]  Mong-Fong Horng,et al.  A Vehicle License Plate Recognition System Based on Spatial/Frequency Domain Filtering and Neural Networks , 2010, ICCCI.