Smart Insights on the Move: Deep Convolution Neural Network and Segmentation for License Plate Recognition
| International Journal of Electronics and Communication Engineering |
| © 2024 by SSRG - IJECE Journal |
| Volume 11 Issue 8 |
| Year of Publication : 2024 |
| Authors : Shajan Jacob, M.K Jeyakumar |
10.14445/23488549/IJECE-V11I8P118
How to Cite?
Shajan Jacob, M.K Jeyakumar, "Smart Insights on the Move: Deep Convolution Neural Network and Segmentation for License Plate Recognition," SSRG International Journal of Electronics and Communication Engineering, vol. 11, no. 8, pp. 172-184, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I8P118
Abstract:
In the modern world, the highway is becoming an increasingly significant part of the entire transportation sector. Due to the widespread interest in Intelligent Transport System technology, numerous systems are being developed and implemented globally. The Intelligent Transport System relies heavily on license plate recognition. Modern technological advancements enable the automatic identification and interpretation of license plate details from images and video streams facilitated by sophisticated license plate recognition systems. These devices are able to reliably read alphanumeric characters from license plates, even in difficult situations like changing angles, lighting, or occlusion. This paper suggests a deep convolutional neural network-based, effective system for license plate recognition. The methodology comprises multiple significant stages, commencing with the acquisition and preprocessing of images via methods like grayscale conversion and thresholding. After that, morphological processes like dilation and erosion are employed to boost the quality of the image, and segmentation is used to separate the area of interest. Next, contour extraction within this segmented area is used to estimate the character. The features that were extracted from the segmented regions are then used to drive a CNN model that accurately recognizes the characters on a license plate. The simulation results confirm that the suggested methodology is effective in correctly identifying and decoding license plate numbers from images, with an impressive recognition accuracy of 99.54%. This method provides an effective and dependable solution for automated license plate recognition tasks. It shows great promise for real-world applications in traffic management, law enforcement, and intelligent transport systems.
Keywords:
Character segmentation, Convolutional neural network, Deep learning, Image processing, Intelligent transportation system, License plate recognition.
References:
[1] Muhammad Alam, Joaquim Ferreira, and José Fonseca, Introduction to Intelligent Transportation Systems, Intelligent Transportation Systems, Studies in Systems, Decision and Control, vol. 52, pp. 1-17, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[2] C.N.E. Anagnostopoulos et al., “A License Plate-Recognition Algorithm for Intelligent Transportation System Applications,” IEEE Transactions on Intelligent Transportation Systems, vol. 7, no. 3, pp. 377-392, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[3] The Tamil Nadu Motor Vehicles Rules, Transportinfo, pp. 1-221, 1989. [Online]. Available: https://transportinfo.in/forms/tnmvr1989.pdf
[4] M. Shridhar et al., “Recognition of License Plate Images: Issues and Perspectives,” Proceedings of the Fifth International Conference on Document Analysis and Recognition, Bangalore, India, pp. 17-20, 1999.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Yi Wang et al., “Rethinking and Designing a High-Performing Automatic License Plate Recognition Approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 8868-8880, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Sergio M. Silva, and Cláudio Rosito Jung, “A Flexible Approach for Automatic License Plate Recognition in Unconstrained Scenarios,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 6, pp. 5693-5703, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Salah Alghyaline, “Real-Time Jordanian License Plate Recognition using deep Learning,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 6, pp. 2601-2609, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Heshan Padmasiri et al., “Automated License Plate Recognition for Resource-Constrained Environments,” Sensors, vol. 22, no. 4, pp. 1-29, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Dilshad Islam, Tanjim Mahmud, and Tanjia Chowdhury, “An Efficient Automated Vehicle License Plate Recognition System Under Image Processing,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 29, no. 2, pp. 1055-1062, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Reda Al-Batat et al., “An End-to-End Automated License Plate Recognition System Using YOLO Based Vehicle and License Plate Detection with Vehicle Classification,” Sensors, vol. 22, no. 23, pp. 1-17, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Imran Shafi et al., “License Plate Identification and Recognition in a Non-Standard Environment Using Neural Pattern Matching,” Complex & Intelligent Systems, vol. 8, pp. 3627-3639, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Jithmi Shashirangana et al., “License Plate Recognition using Neural Architecture Search for Edge Devices,” International Journal of Intelligent Systems, vol. 37, no, 12, pp. 10211-10248, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Alif Ashrafee et al., “Real-Time Bangla License Plate Recognition System for Low Resource Video-Based Applications,” 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), Waikoloa, HI, USA, pp. 479-488, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Anmol Pattanaik, and Rakesh Chandra Balabantaray, “Enhancement of License Plate Recognition Performance using Xception with Mish Activation Function,” Multimedia Tools and Applications, vol. 82, pp. 16793-16815, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Sunil L. Bangare et al., “Reviewing Otsu’s Method for Image Thresholding,” International Journal of Applied Engineering Research, vol. 10, no. 9, pp. 21777-21783, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[16] K.A.M Said, and A.B Jambek, “Analysis of Image Processing using Morphological Erosion and Dilation,” Journal of Physics: Conference Series, vol. 2071, pp. 1-7, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] H.J. He, C. Zheng, and D.W. Sun, Chapter 2 - Image Segmentation Techniques, Computer Vision Technology for Food Quality Evaluation (Second Edition), Academic Press, pp. 45-63, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Jianxin Wu, “Introduction to Convolutional Neural Networks,” National Key Lab for Novel Software Technology, Nanjing University, pp. 1-31, 2017.
[Google Scholar] [Publisher Link]
[19] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, “Deep Learning,” Nature, vol. 521, pp. 436-444, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Hossein Gholamalinezhad, and Hossein Khosravi, “Pooling Methods in Deep Neural Networks, A Review,” arXiv, pp. 1-169, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Hendry, and Rung-Ching Chen, “Automatic License Plate Recognition via Sliding-Window Darknet-YOLO Deep Learning,” Image and Vision Computing, vol. 87, pp. 47-56, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Sergey Zherzdev, and Alexey Gruzdev, “LPRnet: License Plate Recognition via Deep Neural Networks,” Arxiv, pp. 1-6, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Rayson Laroca et al., “A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector,” 2018 International Joint Conference on Neural Networks, Rio de Janeiro, Brazil, pp. 1-10, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Naaman Omar et al., “Fused Faster RCNNs for Efficient Detection of the License Plates,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 19, no. 2, pp. 974-982, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[25] İbrahim Türkyılmaz, and Kirami Kaçan, “License Plate Recognition System using Artificial Neural Networks,” ETRI Journal, vol. 39, no. 2, pp. 163-172, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Irina Valeryevna Pustokhina et al., “Automatic Vehicle License Plate Recognition using Optimal K-means with Convolutional Neural Network for Intelligent Transportation Systems,” IEEE Access, vol. 8, pp. 92907-92917, 2020.
[CrossRef] [Google Scholar] [Publisher Link]