Enhanced Speed & License Plate Recognition with AI Fusion

References

[1] R. A. Lotufo, A. D. Morgan, and A. S. Johnson, “Automatic number-plate recognition,” in IEEE Colloquium on Image Analysis for Transport Applications, Feb 1990, pp. 1–6.

[2] K. Kanayama, Y. Fujikawa, K. Fujimoto, and M. Horino, “Development of workplace number recognition system using real-time image processing and its application to travel-time measurement,” in IEE Vehicular Technology Conference, May 1991, pp. 798–804.

[3] C. N. E. Anagnostopoulos, I. E. Anagnostopoulos, I. D. Psoroulas, and V. Loumos, “License plate recognition from still images and video sequences: A survey,” IEEE Transactions on Intelligent Transportation Systems, vol. 9, no. 3, pp. 377–391, 2008.

[4] S. Du, M. Ibrahim, M. Shehata, and W. Badawy, “Automatic license plate recognition (ALPR): A state-of-the-art review,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 2, pp. 311–325, Feb 2013.

[5] G. G. Rajput and M. T. Erol, “License plate recognition based on temporal redundancy,” in IEEE International Conference on Transportation Systems (ITSC), Nov 2016, pp. 2577–2582.

[6] S. M. Silva and C. R. Jung, “Real-time Brazilian license plate detection and recognition using deep convolutional neural networks,” in Conference on Graphics, Patterns and Images (SIBGRAPI), Oct 2017, pp. 55–62.

[7] S. M. Silva and C. R. Jung, “License plate detection and recognition in unconstrained scenarios,” in European Conference on Computer Vision, Sept 2018, pp. 593–609.

[8] G. Gonçalves, M. A. Diniz, J. Rasera, D. Menotti, and W. R. Schwartz, “Realtime automatic license plate recognition through deep multi-task networks,” in Conference on Graphics, Patterns and Images, Oct 2018, pp. 110–117.

[9] O. Bulan, V. Kozitsky, P. Ramesh, and M. Shreve, “Segmentation- and attention-based license plate recognition with deep localization and failure identification,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 9, pp. 2351–2363, Sept 2017.

[10] J. Spa¨rck, J. Sochor, R. Jur´anek, A. Herout, L. M.´rˇs´ek and P. Zemˇc´ık, “Holistic recognition of low-quality license plates by CNN using track annotated data,” in IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Aug 2017, pp. 1–6.

[11] G. Gonçalves, M. A. Diniz, J. Rasera, D. Menotti, and W. R. Schwartz, “Multitask learning for low-resolution license plate recognition,” in Iberoamerican Congress on Pattern Recognition (CIARP), Oct 2019, pp. 251–261.

[12] H. Li, P. Wang, M. You, and C. Shen, “Reading car license plates using deep neural networks,” Image and Vision Computing, vol. 72, pp. 14–23, 2018.

[13] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.

[14] M. Dong, D. He, C. Luo, D. Liu, and W. Zeng, “A CNN-based approach for automatic license plate recognition in the wild,” in British Machine Vision Conference (BMVC), September 2017, pp. 1–12.

[15] H. Li, P. Wang, and C. Shen, “Toward end-to-end car license plate detection and recognition with deep neural networks,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–11, 2018.

[16] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016, pp. 779–788.

[17] R. Laroca, E. Severo, L. A. Zanlorensi, L. S. Oliveira, G. R. Gonçalves, W. R. Schwartz, and D. Menotti, “A robust real-time automatic license plate recognition based on the YOLO detector,” in International Joint Conference on Neural Networks (IJCNN), July 2018, pp. 1–10.

[18] Y. Kessentini, M. D. Besbes, S. Ammar, and A. Chabbou, “A two-stage deep neural network for multi-norm license plate detection and recognition,” Expert Systems with Applications, vol. 136, pp. 159–170, 2019.

@article{1716379,
      author = {Shaik Mohammed Afroz, Shaik Shoaib, Shaik Arshad Ahmad, N. Radha},
      title = {Enhanced Speed & License Plate Recognition with AI Fusion},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {4474-4501},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1716379.pdf},
      abstract = {This paper presents an efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the state-of-the-art YOLO object detector that contains a unified approach for license plate (LP) detection and layout classification to improve the recognition results using post-processing rules. The system is conceived by evaluating and optimizing different models, aiming at achieving the best speed/accuracy trade-off at each stage. The networks are trained using images from several datasets, with the addition of various data augmentation techniques, so that they are robust under different conditions. The proposed system achieved an average end-to-end recognition rate of 96.9% across eight public datasets (from five different regions) used in the experiments, outperforming both previous works and commercial systems in the ChineseLP, OpenALPR-EU, SSIG-SegPlate and UFPR-ALPR datasets. In the other datasets, the proposed approach achieved competitive results to those attained by the baselines. Our system also achieved impressive frames per second (FPS) rates on a high-end GPU, being able to perform in real time even when there are four vehicles in the scene. An additional contribution is that we manually labeled 38,351 bounding boxes in 6,239 images from public datasets and made the annotations publicly available to the research community.},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1716379}
  }