Automatic Number Plate Recognition of Saudi License Car Plates
Received: 30 December 2021 | Revised: 21 January 2022 | Accepted: 25 January 2022 | Online: 9 April 2022
Automatic license plate recognition has become a significant tool as a result of the development of smart cities. During the experiment studied in the current paper, 50 images were used to detect Saudi car plates. After the preprocessing stage, the canny edge method to detect the car edges and different threshold techniques were used to reduce noise. Horizontal projection was applied in the segmentation process to split the plate. After that, a masking technique was utilized to locate and separate the region of interest in the image. OCR was applied to the processed images to read the characters and numbers in English and Arabic separately. Then, combining the English and Arabic text, after using the re-shaper for the Arabic letters. Finally, rendering of the results of text on images down the plate regions took place. The canny algorithm with projection technique with a proper preprocessing for images produces results with accuracy of 92.4% and 96% for Arabic and English language respectively.
Keywords:Computer Vision, Edge Detection, Segmentation, OCR, License Plate, Recognition System
N. O. Yaseen, S. Ganim Saeed Al-Ali, and A. Sengur, "Development of New Anpr Dataset for Automatic Number Plate Detection and Recognition in North of Iraq," in 1st International Informatics and Software Engineering Conference, Ankara, Turkey, Nov. 2019, pp. 1–6. DOI: https://doi.org/10.1109/UBMYK48245.2019.8965512
N. Rana and P. Dahiya, "Localization Techniques in ANPR Systems: A-State-of-Art," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 7, no. 5, pp. 682–686, Feb. 2017. DOI: https://doi.org/10.23956/ijarcsse/SV7I5/0338
B. A. Hussain and M. S. Hathal, "Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection," Baghdad Science Journal, vol. 17, no. 3, pp. 909–915, 2020. DOI: https://doi.org/10.21123/bsj.2020.17.3.0909
H. Saghaei, "Proposal for Automatic License and Number Plate Recognition System for Vehicle Identification," Nuclear Physics B, vol. 614, no. 3, pp. 467–493, Nov. 2001.
F. Patel, J. Solanki, V. Rajguru, and A. Saxena, "Recognition of Vehicle Number Plate Using Image Processing Technique," Control and Systems Engineering, vol. 2, no. 1, pp. 1–7, Sep. 2018.
T. Duan, D. Tran, P. Tran, and N. Hoang, "Building an Automatic Vehicle License-Plate Recognition System," in International E-Conference on Computer Science, Can Tho, Vietnam, Feb. 2005, pp. 59–63.
M. Salemdeeb and S. Erturk, "Multi-national and Multi-language License Plate Detection using Convolutional Neural Networks," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 5979–5985, Aug. 2020. DOI: https://doi.org/10.48084/etasr.3573
J. Wang, B. Bacic, and W. Q. Yan, "An effective method for plate number recognition," Multimedia Tools and Applications, vol. 77, no. 2, pp. 1679–1692, Jan. 2018. DOI: https://doi.org/10.1007/s11042-017-4356-z
R. Parisi, E. D. Di Claudio, G. Lucarelli, and G. Orlandi, "Car plate recognition by neural networks and image processing," in International Symposium on Circuits and Systems, Monterey, CA, USA, Jun. 1998, vol. 3, pp. 195–198 vol.3.
J. Han and H. Y. Bo, "Analysis and Design of intelligent license Plate recognition system," in 6th International Conference on Intelligent Computing and Signal Processing, Xi’an, China, Apr. 2021, pp. 1079–1082. DOI: https://doi.org/10.1109/ICSP51882.2021.9408766
S. S. Omran and J. A. Jarallah, "Iraqi car license plate recognition using OCR," in Annual Conference on New Trends in Information & Communications Technology Applications, Baghdad, Iraq, Mar. 2017, pp. 298–303. DOI: https://doi.org/10.1109/NTICT.2017.7976127
M. Al-Yaman, H. Alhaj Mustafa, S. Hassanain, A. Abd AlRaheem, A. Alsharkawi, and M. Al-Taee, "Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis," Applied Sciences, vol. 11, no. 22, Jan. 2021, Art. no. 10614. DOI: https://doi.org/10.3390/app112210614
Lubna, N. Mufti, and S. A. A. Shah, "Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms," Sensors, vol. 21, no. 9, Jan. 2021, Art. no. 3028. DOI: https://doi.org/10.3390/s21093028
C. Saravanan, "Color Image to Grayscale Image Conversion," in Second International Conference on Computer Engineering and Applications, Bali, Indonesia, Mar. 2010, vol. 2, pp. 196–199. DOI: https://doi.org/10.1109/ICCEA.2010.192
O. Rukundo and H. Cao, "Nearest Neighbor Value Interpolation," International Journal of Advanced Computer Science and Applications, vol. 3, no. 4, pp. 1–6, 2012. DOI: https://doi.org/10.14569/IJACSA.2012.030405
C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color images," in Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), Bombay, India, Jan. 1998, pp. 839–846.
L. Xuan and Z. Hong, "An improved canny edge detection algorithm," in 8th IEEE International Conference on Software Engineering and Service Science, Beijing, China, Nov. 2017, pp. 275–278. DOI: https://doi.org/10.1109/ICSESS.2017.8342913
O. Vincent and O. Folorunso, "A Descriptive Algorithm for Sobel Image Edge Detection," in Informing Science + IT Education Conference, Macon, United States, Jun. 2009, vol. 9, pp. 97–107. DOI: https://doi.org/10.28945/3351
S. Israni and S. Jain, "Edge detection of license plate using Sobel operator," in International Conference on Electrical, Electronics, and Optimization Techniques, Chennai, India, Mar. 2016, pp. 3561–3563. DOI: https://doi.org/10.1109/ICEEOT.2016.7755367
N. Basheer, A. Aaref, and D. Ayyed, "Digital Image Sobel Edge Detection Using FPGA," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 5, no. 7, pp. 183–190, Apr. 2015.
H. Shimpi, M. Dhage, A. S. Pawar, and N. Gaikwad, "Implementation of Edge Detection Algorithm Using FPGA," International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 4, no. 4, pp. 2193–2197.
R. Dhar, R. Gupta, and K. L. Baishnab, "An analysis of Canny and Laplacian of Gaussian image filters in regard to evaluating retinal image," in International Conference on Green Computing Communication and Electrical Engineering, Coimbatore, India, Mar. 2014, pp. 1–6. DOI: https://doi.org/10.1109/ICGCCEE.2014.6922270
S. Gupta and N. Mohan, "Color Channel Characteristics (CCC) for Efficient Digital Image Forensics," Engineering, Technology & Applied Science Research, vol. 8, no. 1, pp. 2555–2561, Feb. 2018. DOI: https://doi.org/10.48084/etasr.1744
W. Rong, Z. Li, W. Zhang, and L. Sun, "An improved Canny edge detection algorithm," in IEEE International Conference on Mechatronics and Automation, Tianjin, China, Aug. 2014, pp. 577–582. DOI: https://doi.org/10.1109/ICMA.2014.6885761
M. K. Mahto, K. Bhatia, and R. K. Sharma, "Combined horizontal and vertical projection feature extraction technique for Gurmukhi handwritten character recognition," in International Conference on Advances in Computer Engineering and Applications, Ghaziabad, India, Mar. 2015, pp. 59–65. DOI: https://doi.org/10.1109/ICACEA.2015.7164646
C. Patel, A. Patel, and D. Patel, "Optical Character Recognition by Open source OCR Tool Tesseract: A Case Study," International Journal of Computer Applications, vol. 55, no. 10, pp. 50–56, Jul. 2012. DOI: https://doi.org/10.5120/8794-2784
F. Mohammad, J. Anarase, M. Shingote, and P. Ghanwat, "Optical Character Recognition Implementation Using Pattern Matching," International Journal of Computer Science and Information Technologies, vol. 5, no. 2, pp. 2088–2090, 2014.
T. Kluyver et al., "Jupyter Notebooks – a publishing format for reproducible computational workflows," in Positioning and Power in Academic Publishing: Players, Agents and Agendas, F. Loizides and B. Scmidt, Eds. Amsterdam, Netherlands: IOS Press, 2016, pp. 87–90.
B. Ahmed, G. Ali, A. Hussain, A. Baseer, and J. Ahmed, "Analysis of Text Feature Extractors using Deep Learning on Fake News," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 7001–7005, Apr. 2021. DOI: https://doi.org/10.48084/etasr.4069
How to Cite
MetricsAbstract Views: 1082
PDF Downloads: 533
Copyright (c) 2022 R. Antar, S. Alghamdi, J. Alotaibi, M. Alghamdi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.