ARCHIVES
Case Study
Analysis of Text Detection and Extraction Using Deep Learning and SVMs
B Kiran Kumar Reddy1
Surendra HR2
Yashwantha CR3
1 2 3 Department of Computer Science Engineering in Data Science, Dayananda Sagar Academy of Technology and Management, Bengaluru, Karnataka, India.
Published Online: September-December 2025
Pages: 62-66
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403012
References
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Int. Conf. Comput. Eng. Netw. (CENet2017), 2018, pp. 455–463. [Online]. Available: https://doi.org/10.1007/978-981-10-8863- 6_53
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5. J. Liu, H. Wang, and C. Liu, "Text feature extraction based on deep learning: A review," EURASIP J. Wirel. Commun. Netw., vol. 2021,
no. 1, pp. 1–19, 2021. [Online]. Available: https://doi.org/10.1186/s13638- 021- 01965-5
6. T. Villmann, A. Bohnsack, and M. Kaden, "Can learning vector quantization be an alternative to SVM and deep learning? Recent trends and advanced variants of learning vector quantization for classification learning," J. Artif. Intell. Soft Comput Res., vol. 7, no. 1, pp. 65–81, 2017. [Online]. Available: https://doi.org/10.1515/jaiscr- 2017-0005
7. Y. Tang, "Deep learning using linear support vector machines," arXivpreprintv arXiv:1306.0239v4,2015.[Online].A vailable:https://arxiv.org/abs/1306.0 239
8. [Y. Baek, B. Lee, D. Han, S. Yun, and H. Lee, "Character Region Awareness for Text Detection," in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), 2019, pp. 9365– 9374.Proc. Int. Conf. Electron. Renew. Syst. (ICEARS), 2022, pp. 1201–1207. [Online].Available: https://doi.org/10.1109/ICEARS53579.2022 .9752274
9. K. T. Krishnan, "Classification of diabetes using deep learning and SVM techniques," Int. J. Curr. Res. Rev., vol. 13, no.1, pp.146–151,2021. [Online]. Available: https://doi.org/10.31782/IJCRR.2021.13127
10. Y. Tang, "Deep learning using support vector machines," Preprint under review by ICML, 2013.
11. R.-C. Chang, "Intelligent text detection and extraction from natural scene images," Asia University, Taiwan. [IEEE Licensed Copy from Xplore].
12. Y. Zhu, C. Yao, and X. Bai, "Scene text detection and recognition: Recent advances and future trends," Front. Comput. Sci., vol. 10, no. 1, pp. 19–36, 2016. [Online]. Available: https://doi.org/10.1007/s11704- 015- 4488-0
13. S. Surana, V. Shrivastava, K. Pathak, T. R. Mahesh, M. Gagnani, and S. G. Madhuri, "Text extraction and detection from images using machine learning techniques: A research review," in Proc. Int. Conf. Electron. Renew. Syst. (ICEARS), 2022, pp. 1201–1207.[Online].Available: https://doi.org/10.1109/ICEARS5357 9.2022 .9752274
14. X. Zhou, C. Yao, H. Wen, Y. Wang, S. Zhou, W. He, and J. Cai, “EAST: An efficient and accurate scene text detector,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2017, pp. 5551–5560. [Online]. Available: https://ieeexplore.ieee.org/document/8099766
15. Z. Tian, W. Huang, T. He, P. He, and Y. Qiao, “Detecting text in natural image with connectionist text proposal network,” in Proc. 24th Int. Conf. Pattern Recognit. (ICPR), 2016, pp. 2988–2991. [Online]. Available: https: //link.springer.com/chapter/10.1007/978-3-319- 46484-8_4
Eng., vol. 2020, Art. no. 2365076, 2020. [Online]. Available: https://doi.org/10.1155/2020/2365076
2. F. Zhang, P. Yang, H. Lin, and F. Zhang, "Scene text recognition based on bidirectional LSTM and deep neural network," in Proc. 7th
Int. Conf. Comput. Eng. Netw. (CENet2017), 2018, pp. 455–463. [Online]. Available: https://doi.org/10.1007/978-981-10-8863- 6_53
3. A. Kaur and A. Malhotra, "DeepSSR: A deep learning system for structuredrecognition of text images from unstructured paper-based medical reports," in Proc. 2020 11th Int. Conf. Comput., Commun. Netw. Technol. (ICCCNT), 2020, pp. 1–6. [Online]. Available: https://doi.org/10.1109/ICCCNT49239.2020 .9225573
4. G. Shobana and P. Karthikeyan, "Text extraction from video using deep learning," in Proc. 2020 Int. Conf. Comput. Commun. Informat. (ICCCI), 2020, pp. 1–6. [Online]. Available: https://doi.org/10.1109/ICCCI48352. 2020.9 104100
5. J. Liu, H. Wang, and C. Liu, "Text feature extraction based on deep learning: A review," EURASIP J. Wirel. Commun. Netw., vol. 2021,
no. 1, pp. 1–19, 2021. [Online]. Available: https://doi.org/10.1186/s13638- 021- 01965-5
6. T. Villmann, A. Bohnsack, and M. Kaden, "Can learning vector quantization be an alternative to SVM and deep learning? Recent trends and advanced variants of learning vector quantization for classification learning," J. Artif. Intell. Soft Comput Res., vol. 7, no. 1, pp. 65–81, 2017. [Online]. Available: https://doi.org/10.1515/jaiscr- 2017-0005
7. Y. Tang, "Deep learning using linear support vector machines," arXivpreprintv arXiv:1306.0239v4,2015.[Online].A vailable:https://arxiv.org/abs/1306.0 239
8. [Y. Baek, B. Lee, D. Han, S. Yun, and H. Lee, "Character Region Awareness for Text Detection," in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), 2019, pp. 9365– 9374.Proc. Int. Conf. Electron. Renew. Syst. (ICEARS), 2022, pp. 1201–1207. [Online].Available: https://doi.org/10.1109/ICEARS53579.2022 .9752274
9. K. T. Krishnan, "Classification of diabetes using deep learning and SVM techniques," Int. J. Curr. Res. Rev., vol. 13, no.1, pp.146–151,2021. [Online]. Available: https://doi.org/10.31782/IJCRR.2021.13127
10. Y. Tang, "Deep learning using support vector machines," Preprint under review by ICML, 2013.
11. R.-C. Chang, "Intelligent text detection and extraction from natural scene images," Asia University, Taiwan. [IEEE Licensed Copy from Xplore].
12. Y. Zhu, C. Yao, and X. Bai, "Scene text detection and recognition: Recent advances and future trends," Front. Comput. Sci., vol. 10, no. 1, pp. 19–36, 2016. [Online]. Available: https://doi.org/10.1007/s11704- 015- 4488-0
13. S. Surana, V. Shrivastava, K. Pathak, T. R. Mahesh, M. Gagnani, and S. G. Madhuri, "Text extraction and detection from images using machine learning techniques: A research review," in Proc. Int. Conf. Electron. Renew. Syst. (ICEARS), 2022, pp. 1201–1207.[Online].Available: https://doi.org/10.1109/ICEARS5357 9.2022 .9752274
14. X. Zhou, C. Yao, H. Wen, Y. Wang, S. Zhou, W. He, and J. Cai, “EAST: An efficient and accurate scene text detector,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2017, pp. 5551–5560. [Online]. Available: https://ieeexplore.ieee.org/document/8099766
15. Z. Tian, W. Huang, T. He, P. He, and Y. Qiao, “Detecting text in natural image with connectionist text proposal network,” in Proc. 24th Int. Conf. Pattern Recognit. (ICPR), 2016, pp. 2988–2991. [Online]. Available: https: //link.springer.com/chapter/10.1007/978-3-319- 46484-8_4
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