Studies Advanced in License Plate Recognition

Research Article
Open access

Studies Advanced in License Plate Recognition

Bintao Yang 1*
  • 1 South China Normal University    
  • *corresponding author 20202005446@scnu.edu.cn
Published on 1 August 2023 | https://doi.org/10.54254/2755-2721/8/20230257
ACE Vol.8
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-63-8
ISBN (Online): 978-1-915371-64-5

Abstract

License plate recognition is a crucial mission in computer vision, and deep learning has significantly improved its performance. A representative license plate recognition system involves five components: license plate image preprocessing, image acquisition, license plate detection, character recognition, and character segmentation. This paper will explore the methods commonly used in each stage of the recognition process and analyze some of the current challenges and future trends of license plate recognition. This discussion will consider real-world factors such as lighting and weather conditions that can affect recognition accuracy. Ultimately, it is hoped that these insights will contribute to the development of intelligent transportation systems.

Keywords:

license plate recognition, automatic recognition, license plate positioning, character recognition, character segmentation

Yang,B. (2023). Studies Advanced in License Plate Recognition. Applied and Computational Engineering,8,494-500.
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References

[1]. D.R. Vedhaviyassh, R. Sudhan, G. Saranya, M. Safa, D. Arun, "Comparative Analysis of EasyOCR and TesseractOCR for Automatic License Plate Recognition using Deep Learning Algorithm", 2022 6th International Conference on Electronics, Communication and Aerospace Technology, pp.966-971, 2022.

[2]. Fabrizio De Vita, Giorgio Nocera, Orlando Marco Belcore, Antonio Polimeni, Francesco Longo, Dario Bruneo, Massimo Di Gangi, "Traffic Condition Estimation at the Smart City Edge using Deep Learning: A Ro-Pax Terminal Case Study", 2022 IEEE International Smart Cities Conference (ISC2), pp.1-7, 2022.

[3]. Huei-Yung Lin, Cheng-Yu Ho, "Adaptive Speed Bump With Vehicle Identification for Intelligent Traffic Flow Control", IEEE Access, vol.10, pp.68009-68016, 2022.

[4]. Md. Iqbal Hossain, Raghib Barkat Muhib, Amitabha Chakrabarty, "Identifying Bikers Without Helmets Using Deep Learning Models", 2021 Digital Image Computing: Techniques and Applications (DICTA), pp.01-08, 2021.

[5]. J.Andrew Onesimu, Robin D.Sebastian, Yuichi Sei, Lenny Christopher, "An Intelligent License Plate Detection and Recognition Model Using Deep Neural Networks", Annals of Emerging Technologies in Computing, vol.5, no.4, pp.23, 2021.

[6]. Modou Gueye, "Learning Color Transitions to Extract Senegalese License Plates", Research in Computer Science and Its Applications, vol.400, pp.28, 2021.

[7]. Musa Al-Yaman, Haneen Alhaj Mustafa, Sara Hassanain, Alaa Abd AlRaheem, Adham Alsharkawi, Majid Al-Taee, "Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis", Applied Sciences, vol.11, no.22, pp.10614, 2021.

[8]. Huijie Zhang, Li An, Vena W. Chu, Douglas A. Stow, Xiaobai Liu, Qinghua Ding, "Learning Adjustable Reduced Downsampling Network for Small Object Detection in Urban Environments", Remote Sensing, vol.13, no.18, pp.3608, 2021.

[9]. Chun-Liang Tung, Ching-Hsin Wang, Bo-Syuan Peng, "A Deep Learning Model of Dual-Stage License Plate Recognition Applicable to the Data Processing Industry", Mathematical Problems in Engineering, vol.2021, pp.1, 2021.

[10]. Khurram Khan, Abid Imran, Hafiz Zia Ur Rehman, Adnan Fazil, Muhammad Zakwan, Zahid Mahmood, "Performance enhancement method for multiple license plate recognition in challenging environments", EURASIP Journal on Image and Video Processing, vol.2021, no.1, 2021.

[11]. Jithmi Shashirangana, Heshan Padmasiri, Dulani Meedeniya, Charith Perera, Soumya R. Nayak, Janmenjoy Nayak, Shanmuganthan Vimal, Seifidine Kadry, "License plate recognition using neural architecture search for edge devices", International Journal of Intelligent Systems, vol.37, no.12, pp.10211, 2022.

Cite this article

Yang,B. (2023). Studies Advanced in License Plate Recognition. Applied and Computational Engineering,8,494-500.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning

ISBN：978-1-915371-63-8(Print) / 978-1-915371-64-5(Online)
Editor：Anil Fernando, Marwan Omar
Conference website: http://www.confseml.org
Conference date: 19 April 2023
Series: Applied and Computational Engineering
Volume number: Vol.8
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. D.R. Vedhaviyassh, R. Sudhan, G. Saranya, M. Safa, D. Arun, "Comparative Analysis of EasyOCR and TesseractOCR for Automatic License Plate Recognition using Deep Learning Algorithm", 2022 6th International Conference on Electronics, Communication and Aerospace Technology, pp.966-971, 2022.

[2]. Fabrizio De Vita, Giorgio Nocera, Orlando Marco Belcore, Antonio Polimeni, Francesco Longo, Dario Bruneo, Massimo Di Gangi, "Traffic Condition Estimation at the Smart City Edge using Deep Learning: A Ro-Pax Terminal Case Study", 2022 IEEE International Smart Cities Conference (ISC2), pp.1-7, 2022.

[3]. Huei-Yung Lin, Cheng-Yu Ho, "Adaptive Speed Bump With Vehicle Identification for Intelligent Traffic Flow Control", IEEE Access, vol.10, pp.68009-68016, 2022.

[4]. Md. Iqbal Hossain, Raghib Barkat Muhib, Amitabha Chakrabarty, "Identifying Bikers Without Helmets Using Deep Learning Models", 2021 Digital Image Computing: Techniques and Applications (DICTA), pp.01-08, 2021.

[5]. J.Andrew Onesimu, Robin D.Sebastian, Yuichi Sei, Lenny Christopher, "An Intelligent License Plate Detection and Recognition Model Using Deep Neural Networks", Annals of Emerging Technologies in Computing, vol.5, no.4, pp.23, 2021.

[6]. Modou Gueye, "Learning Color Transitions to Extract Senegalese License Plates", Research in Computer Science and Its Applications, vol.400, pp.28, 2021.

[7]. Musa Al-Yaman, Haneen Alhaj Mustafa, Sara Hassanain, Alaa Abd AlRaheem, Adham Alsharkawi, Majid Al-Taee, "Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis", Applied Sciences, vol.11, no.22, pp.10614, 2021.

[8]. Huijie Zhang, Li An, Vena W. Chu, Douglas A. Stow, Xiaobai Liu, Qinghua Ding, "Learning Adjustable Reduced Downsampling Network for Small Object Detection in Urban Environments", Remote Sensing, vol.13, no.18, pp.3608, 2021.

[9]. Chun-Liang Tung, Ching-Hsin Wang, Bo-Syuan Peng, "A Deep Learning Model of Dual-Stage License Plate Recognition Applicable to the Data Processing Industry", Mathematical Problems in Engineering, vol.2021, pp.1, 2021.

[10]. Khurram Khan, Abid Imran, Hafiz Zia Ur Rehman, Adnan Fazil, Muhammad Zakwan, Zahid Mahmood, "Performance enhancement method for multiple license plate recognition in challenging environments", EURASIP Journal on Image and Video Processing, vol.2021, no.1, 2021.

[11]. Jithmi Shashirangana, Heshan Padmasiri, Dulani Meedeniya, Charith Perera, Soumya R. Nayak, Janmenjoy Nayak, Shanmuganthan Vimal, Seifidine Kadry, "License plate recognition using neural architecture search for edge devices", International Journal of Intelligent Systems, vol.37, no.12, pp.10211, 2022.

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