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Original Article
AI-Based Automated Race Winner Detection System
Dr. K. Rajakumari1
Poovizhi S2
Samson J3
Mukesh M4
Dinesh karthik K5
1 2 3 4 5 Computer Science and Engineering, United Institute Of Technology, Coimbatore, Tamil Nadu, India.
Published Online: May-August 2026
Pages: 336-346
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502037
References
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2. R. Smith, "An Overview of the Tesseract OCR Engine," in Proc. 9th IEEE Int. Conf. Document Analysis and Recognition (ICDAR), Curitiba, Brazil, Sep. 2007, pp. 629–633.
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7. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional NeuralNetworks," Commun. ACM, vol. 60, no. 6, pp. 84–90, Jun. 2017.
8. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time ObjectDetection," in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, Jun. 2016, pp. 779–788.
9. K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," inProc. Int. Conf. Learning Representations (ICLR), San Diego, CA, May 2015.
10. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, Jun. 2018, pp. 4510–4520.
11. T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, "Focal Loss for Dense Object Detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 2, pp. 318–327, Feb. 2020.
12. S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997.
13. Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, no. 7553, pp. 436–444, May 2015.
14. A. Graves, S. Ferna´ndez, and J. Schmidhuber, "Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition," in Proc. 15th Int. Conf. Artificial Neural Networks (ICANN), Warsaw, Sep. 2005, pp. 799–804.
15. D. P. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization," in Proc. 3rd Int. Conf. Learning Representations (ICLR), San Diego, CA, May 2015.
16. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: A Simple Way toPrevent Neural Networks from Overfitting," J. Mach. Learn. Res., vol. 15, no. 1, pp. 1929–1958, Jun. 2014.
17. S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network Training by Reducing InternalCovariate Shift," in Proc. 32nd Int. Conf. Machine Learning (ICML), Lille, France, Jul. 2015, pp. 448–456.
18. K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, Jun. 2016, pp. 770–778.
2. R. Smith, "An Overview of the Tesseract OCR Engine," in Proc. 9th IEEE Int. Conf. Document Analysis and Recognition (ICDAR), Curitiba, Brazil, Sep. 2007, pp. 629–633.
3. Google ML Kit Documentation. Google Developers. [Online]. Available: https://developers.google.com/ml-kit. Accessed: Mar. 2026.
4. Flutter Framework Documentation. Google. [Online]. Available: https://flutter.dev/docs. Accessed: Mar. 2026.
5. C. Lugaresi et al., "MediaPipe: A Framework for Building Perception Pipelines," arXiv preprint arXiv: 1906.08172, Jun. 2019.
6. Z. Cao, G. Hidalgo, T. Simon, S. Wei, and Y. Sheikh, "OpenPose: Realtime Multi-Person 2D Pose EstimationUsing Part Affinity Fields," IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 1, pp. 172–186, Jan. 2021.
7. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional NeuralNetworks," Commun. ACM, vol. 60, no. 6, pp. 84–90, Jun. 2017.
8. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time ObjectDetection," in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, Jun. 2016, pp. 779–788.
9. K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," inProc. Int. Conf. Learning Representations (ICLR), San Diego, CA, May 2015.
10. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, Jun. 2018, pp. 4510–4520.
11. T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, "Focal Loss for Dense Object Detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 2, pp. 318–327, Feb. 2020.
12. S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997.
13. Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, no. 7553, pp. 436–444, May 2015.
14. A. Graves, S. Ferna´ndez, and J. Schmidhuber, "Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition," in Proc. 15th Int. Conf. Artificial Neural Networks (ICANN), Warsaw, Sep. 2005, pp. 799–804.
15. D. P. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization," in Proc. 3rd Int. Conf. Learning Representations (ICLR), San Diego, CA, May 2015.
16. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: A Simple Way toPrevent Neural Networks from Overfitting," J. Mach. Learn. Res., vol. 15, no. 1, pp. 1929–1958, Jun. 2014.
17. S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network Training by Reducing InternalCovariate Shift," in Proc. 32nd Int. Conf. Machine Learning (ICML), Lille, France, Jul. 2015, pp. 448–456.
18. K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, Jun. 2016, pp. 770–778.
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