ARCHIVES
Original Article
Stolen Vehicle Detection Using YOLO and OCR in a Stream Lit-Based Web Interface
Sultan Ba Ata1
Dr. Khaja Mahabubullah2
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.
Published Online: September-October 2025
Pages: 29-34
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20250505006
References
1. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016, pp. 779–788.
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.
Related Articles
2025
A Comprehensive Review on Antibiotic Resistance
2025
AI-Driven Conversational Models for Supporting Migrant Career Guidance and Labour Market Integration: A Scoping Review
2025
Cloud-Based MIS Framework for Streamlining Outcome-Based Education Evaluation in Higher Education
2025
A Scalable System Design for Real-Time Personalized Recommendation Engines in E-Commerce
2025
AI-Powered Career Advisor (A Personalized Career Guidance System)
2025
