International Journal of Intelligent Systems and Applications(IJISA)
ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)
Published By: MECS Press
IJISA Vol.8, No.3, Mar. 2016
An Automatic Number Plate Recognition System under Image Processing
Full Text (PDF, 1176KB), PP.14-25
Automatic Number Plate Recognition system is an application of computer vision and image processing technology that takes photograph of vehicles as input image and by extracting their number plate from whole vehicle image , it display the number plate information into text. Mainly the ANPR system consists of 4 phases: - Acquisition of Vehicle Image and Pre-Processing, Extraction of Number Plate Area, Character Segmentation and Character Recognition. The overall accuracy and efficiency of whole ANPR system depends on number plate extraction phase as character segmentation and character recognition phases are also depend on the output of this phase. Further the accuracy of Number Plate Extraction phase depends on the quality of captured vehicle image. Higher be the quality of captured input vehicle image more will be the chances of proper extraction of vehicle number plate area. The existing methods of ANPR works well for dark and bright/light categories image but it does not work well for Low Contrast, Blurred and Noisy images and the detection of exact number plate area by using the existing ANPR approach is not successful even after applying existing filtering and enhancement technique for these types of images. Due to wrong extraction of number plate area, the character segmentation and character recognition are also not successful in this case by using the existing method. To overcome these drawbacks I proposed an efficient approach for ANPR in which the input vehicle image is pre-processed firstly by iterative bilateral filtering , adaptive histogram equalization and number plate is extracted from pre-processed vehicle image using morphological operations, image subtraction, image binarization/thresholding, sobel vertical edge detection and by boundary box analysis. Sometimes the extracted plate area also contains noise, bolts, frames etc. So the extracted plate area is enhanced by using morphological operations to improve the quality of extracted plate so that the segmentation phase gives more successful output. The character segmentation is done by connected component analysis and boundary box analysis and finally in the last character recognition phase, the characters are recognized by matching with the template database using correlation and output results are displayed. This approach works well for low contrast, blurred, noisy as well as for dark and light/bright category images. The comparison is done between the ANPR with Adaptive Histogram Equalization and Iterative Bilateral Filtering that is the proposed approach and the existing ANPR approach using metrics: MSE, PSNR and Success rate.
Cite This Paper
Sarbjit Kaur,"An Automatic Number Plate Recognition System under Image Processing", International Journal of Intelligent Systems and Applications(IJISA), Vol.8, No.3, pp.14-25, 2016. DOI: 10.5815/ijisa.2016.03.02
Christos-Nikolaos E. Anagnostopoulos, “License Plate Recognition: A Brief Tutorial”, Intelligent Transportation Systems Magazine (IEEE), Vol.6, Issue.1, pp.59-67, 2014.
Shan Du,Mahmoud Ibrahim,Mohamed Shehata and Wael Badawy, “Automatic License Plate Recognition (ALPR):A State-of-the-Art Review”, IEEE Transactions on Circuits & Systems for Video Technology, Vol. 23, Issue.2, pp.311-325, 2013.
Sahil Shaikh, Bornika Lahiri, Gopi Bhatt and Nirav Raja,“A novel approach for Automatic Number Plate Recognition”, IEEE International Conference on Intelligent Systems and Signal Processing (ISSP), pp.275-380, 2013.
Norizam Sulaiman,“Development of Automatic Vehicle Plate Detection System”, IEEE International Conference on System Engineering and Technology (ICSET), pp.130-135, 2013.
Reza Azad and Hamid Reza Shayegh,” New Method for Optimization of License Plate Recognition system with Use of Edge Detection and Connected Component”, IEEE International Conference on Computer and Knowledge Engineering (ICCKE), pp. 21-25, 2013.
Ronak P Patel, Narendra M Patel and Keyur Brahmbhatt,“Automatic Licenses Plate Recognition”, International Journal of Computer Science and Mobile Computing (IJCSMC) , Vol. 2, Issue. 4, pp.285-294, 2013.
Najeem Owamoyo, A.Alaba Fadele and Abimbola Abudu, “Number Plate Recognition for Nigerian Vehicles”, Academic Research International Journal (ARIJ), Vol.4, Issue.3, pp.48-55, 2013.
Sourav Roy, Amitava Choudhury and Joydeep Mukherjee, “An Approach towards Detection of Indian Number Plate from Vehicle”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), Vol.2, Issue.4, pp.241-244, 2013.
Divya gilly and Dr. Kumudha Raimond, “License Plate Recognition- A Template Matching Method”, International Journal of Engineering Research and Applications (IJERA), Vol. 3, Issue. 2, pp.1240-1245, 2013.
Isack Bulugu,“Algorithm for License Plate Localization and Recognition for Tanzania Car Plate Numbers”, International Journal of Science and Research (IJSR), Vol. 2, Issue.5, pp.12-16, 2013.
Rupali Kate, “Number Plate Recognition Using Segmentation”, International Journal of Engineering Research & Technology (IJERT), Vol.1, Issue.9, pp.1-5, 2012.
P.Mohan Kumar, P.Kumaresan and Dr.S.A.K.Jilani,” The Real Time Vehicle License Plate Identification System “, International Journal of Engineering Research and Development, Vol. 2, Issue. 4, pp.35-39, 2012.
Shoaib Rehman Soomro, Mohammad Arslan and Javed Fahad Ahmed,“Vehicle Number Plate Recognition System for Automatic Toll Tax Collection", IEEE International Conference on Robotics and Artificial Intelligence (ICRAI), pp.125-129, 2012.
Xiaojun Zhai, Faycal Bensaali and Reza Sotudeh,“OCR-Based Neural Network for ANPR", IEEE International Conference on Imaging Systems and Techniques (IST), pp. 393-397, 2012.
Hadi Sharifi Kolour,“An Evaluation of License Plate Recognition Algorithms”, International Journal of Digital Information and Wireless Communications, ISSN 2225-658X, pp.247-253, 2011.
L N P Boggavarapu , J K Munagala, R S Vaddi and K R Anne,“Localization of Non-Standard License Plate using Morphological Operations- An Indian Context”, IEEE International Conference on Electronics Computer Technology (CECT), Vol.3, pp. 65-69, 2011.
Kumar Parasuraman and P.Vasantha Kumar,“An Efficient Method for Indian Vehicle License Plate Extraction and Character Segmentation”, IEEE International Conference on Computational Intelligence and Computing Research, pp. 1475-1477, 2010.
S. Hamidreza Kasaei, S. Mohammadreza Kasaei and S. Alireza Kasaei,“New Morphology-Based Method for Robust Iranian Car Plate Detection and Recognition”, International Journal of Computer Theory and Engineering, Vol. 2, No. 2, pp.264-268, 2010.
Chirag N. Paunwala, Dr. Suprava Patnaik and Manoj Chaudhary,“Multiple License Plate Detection Algorithm Based on Mathematical Morphology and Component Filtering”, IEEE International Conference on Advances in Recent Technologies in Communication and Computing, pp.240-242, 2010.
Satadal Saha, Subhadip Basu, Mita Nasipuri and Dipak Kumar Basu,“License Plate Localization from Vehicle Images: An Edge Based Multi-stage Approach”, International Journal of Recent Trends in Engineering, Vol. 1, Issue.1, pp.284-288, 2009.
Sarbjit Kaur, Sukhbir Kaur, “An Efficient Method of Number Plate Extraction form Indian Vehicles Image”, International Journal of Computer Applications (IJCA), Vol.88, Issue.4, pp.14-19, February 2014.
Sarbjit Kaur, Sukhbir Kaur, “ An Efficient Approach for Number Plate Extraction from Vehicles Image under Image Processing” , International Journal of Computer Science and Information Technology (IJCSIT), Vol.5, Issue.3, pp.2954-2959, 2014.
Sarbjit Kaur, Sukhbir Kaur, “Automatic Number Plate Recognition System under Image Processing: a Review”, International Multi-Track Conference on Sciences, Engineering and Technical Innovations (IMTC-14), 2014.
Sarbjit Kaur, Sukhbir Kaur, “An Efficient Approach for Automatic Number Plate Recognition System under Image Processing”, International Journal of Advanced Research in Computer Science (IJARCS), Vol.5, Issue.6, pp.43-50, July-August 2014.