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- Vol. 5 Issue 5
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Stolen Vehicle Detection Using YOLO and OCR in a Stream Lit-Based Web Interface
Authors
1 Student, MCA, Deccan College of Engineering and Technology, Hyderabed, Telangana, India. 2 Professor & HOD, MCA, Deccan College of Engineering and Technology, Hyderabed, Telangana, India.
Article Information
- Published In
- Volume 5, Issue 5 (2025)
- Published Online
- September-October 2025
- Pages
- 29-34
- Article Type
- Original Article
References
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2. G. Jocher et al., “YOLOv5 by Ultralytics,” GitHub Repository, 2020. [Online]. Available: https://github.com/ultralytics/yolov5
3. A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” arXiv preprint arXiv:2004.10934, 2020.
4. J. Baek, G. Kim, S. Lee, H. Park, and H. Kim, “Character Region Awareness for Text Detection,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), 2019, pp. 9365–9374.
5. K. H. Li, M. Y. Shieh, and H. C. Liu, “Automatic License Plate Recognition System Based on Deep Learning,” IEEE Access, vol. 7, pp. 16996–17006, 2019.
6. Jaided AI, “EasyOCR: A Ready-to-Use OCR with 80+ Languages Supported,” GitHub. [Online]. Available: https://github.com/JaidedAI/EasyOCR
7. R. Smith, “An Overview of the Tesseract OCR Engine,” in Proc. Ninth Int. Conf. Document Anal. Recognit., vol. 2, 2007, pp. 629–633.
8. A. Agarwal and B. B. Gupta, “A Novel Framework for License Plate Recognition Using CNN and Transfer Learning,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 12, pp. 7553–7562, Dec. 2021.
9. Y. Du, J. Bai, and W. Liu, “Vehicle and License Plate Recognition System Based on Multi-Task Learning,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 3, pp. 2512–2523, Mar. 2022.
10. S. Zhou, W. Wen, Z. Cui, and H. Lu, “Real-Time License Plate Detection and Recognition on Embedded Devices,” IEEE Trans. Veh. Technol., vol. 70, no. 6, pp. 5301–5314, Jun. 2021.
2. G. Jocher et al., “YOLOv5 by Ultralytics,” GitHub Repository, 2020. [Online]. Available: https://github.com/ultralytics/yolov5
3. A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” arXiv preprint arXiv:2004.10934, 2020.
4. J. Baek, G. Kim, S. Lee, H. Park, and H. Kim, “Character Region Awareness for Text Detection,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), 2019, pp. 9365–9374.
5. K. H. Li, M. Y. Shieh, and H. C. Liu, “Automatic License Plate Recognition System Based on Deep Learning,” IEEE Access, vol. 7, pp. 16996–17006, 2019.
6. Jaided AI, “EasyOCR: A Ready-to-Use OCR with 80+ Languages Supported,” GitHub. [Online]. Available: https://github.com/JaidedAI/EasyOCR
7. R. Smith, “An Overview of the Tesseract OCR Engine,” in Proc. Ninth Int. Conf. Document Anal. Recognit., vol. 2, 2007, pp. 629–633.
8. A. Agarwal and B. B. Gupta, “A Novel Framework for License Plate Recognition Using CNN and Transfer Learning,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 12, pp. 7553–7562, Dec. 2021.
9. Y. Du, J. Bai, and W. Liu, “Vehicle and License Plate Recognition System Based on Multi-Task Learning,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 3, pp. 2512–2523, Mar. 2022.
10. S. Zhou, W. Wen, Z. Cui, and H. Lu, “Real-Time License Plate Detection and Recognition on Embedded Devices,” IEEE Trans. Veh. Technol., vol. 70, no. 6, pp. 5301–5314, Jun. 2021.
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