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Effective Detection and Localization of the Text in Natural Scene Images Using Adaptive Kuwahara Filter

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Advances in Information Communication Technology and Computing

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 392))

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Abstract

The text matter present in the natural scene images imbibes facts and statistics about the concerned image. The text detected and located in the scene images finds applications in many domains like content retrieval, tourist navigation, etc. The real challenges in this domain are the surrounding elements and other noisy backgrounds present in the images. This paper attempts to detect and localize the text with the help of novel method that involves preprocessing the images using the adaptive Kuwahara filter and MSERs. Classifying the components in text and non-text is achieved with the assistance of classifiers using the MATLAB. Further, the texts are grouped into words with the help of the K-means clustering algorithm. The bounding box constructed for the localization of the text is evaluated with the DetEval tool. The experiments are performed using the training dataset of ICDAR 2013 and the testing dataset of the ICDAR 2011. The results obtained are better than the other state of the arts, concerning the F-measure, recall, and precision.

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References

  1. Zhang, H., Zhao, K., Song, Y.-Z., Guo, J.: Text extraction from natural scene image: a survey. Neurocomputing 122, 310–323 (2013)

    Article  Google Scholar 

  2. Shahab, A., Shafait, F., Dengel, A.: ICDAR 2011 robust reading competition challenge 2: Reading text in scene images. In: 2011 International Conference on Document Analysis and Recognition, pp. 1491–1496. IEEE (2011)

    Google Scholar 

  3. Karatzas, D., Shafait, F., Uchida, S., Iwamura, M., Gomez i Bigorda, L., Mestre, S.R., Mas, J., Mota, D.F., Almazan, J.A., De Las Heras, L.P.: ICDAR 2013 robust reading competition. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 1484–1493. IEEE (2013)

    Google Scholar 

  4. Bartyzel, K.: Adaptive Kuwahara Filter. SIViP 10(4), 663–670 (2016)

    Article  Google Scholar 

  5. The Math Works, Inc. MATLAB, version 2020a. Natick, MA: The Math Works, Inc., 2020. Accessed May 28, 2020. https://www.mathworks.com/

  6. Wolf, C., Jolion, J.-M.: Object count/area graphs for the evaluation of object detection and segmentation algorithms. IJDAR 8(4), 280–296 (2006)

    Article  Google Scholar 

  7. Prasad, V., Das, P.: Recent trends and techniques in text detection and text localization in a natural scene: a survey. ADBU J. Eng. Technol. 10(1) (2021)

    Google Scholar 

  8. Yu, C., Song, Y., Zhang, Y.: Scene text localization using edge analysis and feature pool. Neurocomputing 175, 652–661 (2016)

    Article  Google Scholar 

  9. Epshtein, B., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2963–2970. IEEE (2010)

    Google Scholar 

  10. Li, Y., Lu, H.: Scene text detection via stroke width. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp. 681–684. IEEE (2012)

    Google Scholar 

  11. Guan, L., Chu, C.: Natural scene text detection based on swt, mser and candidate classification. In: 2017 2nd International Conference on Image, Vision and Computing (ICIVC), pp. 26–30. IEEE (2017)

    Google Scholar 

  12. Wu, H., Zou, B., Zhao, Y.-Q., Guo, J.: Scene text detection using adaptive color reduction, adjacent character model and hybrid verification strategy. Vis. Comput. 33(1), 113–126 (2017)

    Article  Google Scholar 

  13. Ghanei, S., Faez, K.: A robust approach for scene text localization using rule-based confidence map and grouping. Int. J. Pattern Recognit Artif Intell. 31(03), 1753002 (2017)

    Article  Google Scholar 

  14. Baran, R., Partila, P., Wilk, R.: Automated text detection and character recognition in natural scenes based on local image features and contour processing techniques. In: International Conference on Intelligent Human Systems Integration, pp. 42–48. Springer, Cham (2018)

    Google Scholar 

  15. Jiang, M., Cheng, J., Chen, M., Ku, X.: An improved text localization method for natural scene images. J. Phys. Conf. Ser. 960(1), 012027 (2018)

    Google Scholar 

  16. Mukhopadhyay, A., Kumar, S., Chowdhury, S.R., Chakraborty, N., Mollah, A.F., Basu, S., Sarkar, R.: Multi-lingual scene text detection using one-class classifier. Int. J. Comput. Vis. Image Process. (IJCVIP) 9(2), 48–65 (2019)

    Google Scholar 

  17. Ch’ng, C.-K., Chan, C.S., Liu, C.-L.: Total-text: toward orientation robustness in scene text detection. Int. J. Document Anal. Recogn. (IJDAR) 23(1), 31–52 (2020)

    Google Scholar 

  18. Liu, J., Zhong, Q., Yuan, Y., Su, H., Du, B.: SemiText: scene text detection with semi-supervised learning. Neurocomputing 407, 343–353 (2020)

    Google Scholar 

  19. Zhu, Y., Du, J.: Textmountain: accurate scene text detection via instance segmentation. Pattern Recogn. 110, 107336 (2021)

    Google Scholar 

  20. Wang, H., Bai, X., Yang, M., Zhu, S., Wang, J., Liu, W.: Scene text retrieval via joint text detection and similarity learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4558–4567 (2021)

    Google Scholar 

  21. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22(10), 761–767 (2004)

    Article  Google Scholar 

  22. Neumann, L., Matas, J.: A method for text localization and recognition in real-world images. In: Asian Conference on Computer Vision, pp. 770–783. Springer, Berlin, Heidelberg (2010)

    Google Scholar 

  23. Soni, R., Kumar, B., Chand, S.: Text detection and localization in natural scene images using MSER and fast guided filter. In: 2017 Fourth International Conference on Image Information Processing (ICIIP), pp. 1–6. IEEE (2017)

    Google Scholar 

  24. Klein, D.A., Frintrop, S.: Center-surround divergence of feature statistics for salient object detection. In: 2011 International Conference on Computer Vision, pp. 2214–2219. IEEE (2011)

    Google Scholar 

  25. Rokach, L., Maimon, O.Z.: Data Mining with Decision Trees: Theory and Applications, vol. 69. World scientific (2007)

    Google Scholar 

  26. Fix, E., Hodges, J.L.: Discriminatory analysis. Nonparametric discrimination: Consistency properties. Int. Statis. Rev./Revue Internationale de Statistique 57(3), 238–247 (1989)

    Google Scholar 

  27. Zhang, Y., Jatowt, A.: Estimating a one-class naive Bayes text classifier. Intell. Data Anal. 24(3), 567–579 (2020)

    Article  Google Scholar 

  28. Tolles, J., Meurer, W.J.: Logistic regression: relating patient characteristics to outcomes. JAMA 316(5), 533–534 (2016)

    Article  Google Scholar 

  29. Li, S.Z., Jain, A.: LDA (linear discriminant analysis). In: Encyclopedia of Biometrics, p. 899. Springer US, Boston, MA, USA (2009)

    Google Scholar 

  30. Tharwat, A.: Linear vs. quadratic discriminant analysis classifier: a tutorial. Int. J. Appl. Pattern Recogn. 3(2), 145–180 (2016)

    Article  Google Scholar 

  31. Drucker, H., Cortes, C.: Boosting decision trees. In: Advances in Neural Information Processing Systems, pp. 479–485 (1996)

    Google Scholar 

  32. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  33. Likas, A., Vlassis, N., Verbeek, J.J.: The global k-means clustering algorithm. Pattern Recogn. 36(2), 451–461 (2003)

    Article  Google Scholar 

  34. Alnefaie, A., Gupta, D., Bhuyan, M.H., Razzak, I., Gupta, P., Prasad, M.: End-to-end analysis for text detection and recognition in natural scene images. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2020)

    Google Scholar 

  35. Wang, Y., Shi, C., Xiao, B., Wang, C., Qi, C.: CRF based text detection for natural scene images using convolutional neural network and context information. Neurocomputing 295, 46–58 (2018)

    Article  Google Scholar 

  36. Joan, Faustina, S.P., Valli, S.: An enhanced text detection technique for the visually impaired to read text. Inf. Syst. Front. 19(5), 1039–1056 (2017)

    Google Scholar 

  37. Feng, Y., Song, Y., Zhang, Y.: Scene text detection based on multi-scale SWT and edge filtering. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 645–650. IEEE (2016)

    Google Scholar 

  38. Lucas, S.M., Panaretos, A., Sosa, L., Tang, A., Wong, S., Young, R., Ashida, K., et al.: ICDAR 2003 robust reading competitions: entries, results, and future directions. Int. J. Document Anal. Recogn. (IJDAR) 7(2–3), 105–122 (2005)

    Google Scholar 

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Authors and Affiliations

  1. Engineering College Bikaner, Bikaner, Rajasthan, India

    Rituraj Soni, Vishal Goar & Manoj Kuri

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  1. Rituraj Soni
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  2. Vishal Goar
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  3. Manoj Kuri
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Editor information

Editors and Affiliations

  1. Government Engineering College Bikaner, Bikaner, Rajasthan, India

    Vishal Goar

  2. Government Engineering College Bikaner, Bikaner, Rajasthan, India

    Manoj Kuri

  3. Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Rajesh Kumar

  4. University of the Ryukyus, Nishihara, Japan

    Tomonobu Senjyu

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Soni, R., Goar, V., Kuri, M. (2022). Effective Detection and Localization of the Text in Natural Scene Images Using Adaptive Kuwahara Filter. In: Goar, V., Kuri, M., Kumar, R., Senjyu, T. (eds) Advances in Information Communication Technology and Computing. Lecture Notes in Networks and Systems, vol 392. Springer, Singapore. https://doi.org/10.1007/978-981-19-0619-0_54

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  • DOI: https://doi.org/10.1007/978-981-19-0619-0_54

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-0618-3

  • Online ISBN: 978-981-19-0619-0

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