Abstract
Identifying the security level of a private record is an indispensable task for associations to ensure the secret data represented. Different rules and systems are being applied by human specialists. Expanding the number of private data in associations are making it hard to order all the reports cautiously with human exertion. In the present advanced period, it turns into a test for users of the web to discover explicit data on the web. Many online records are recovered, and it is difficult to process all the recovered data. The programmed text summary is a procedure that recognizes the significant focuses from all the related archives to deliver a compact outline. Enormous datasets surround covered patterns which pass on significant information about the dataset. Information mining research manages the extraction of helpful and important data from such huge datasets. The procedure of information extraction can be seen as exploration and examination of huge amounts of information, via programmed or self-loader mean, to find significant examples and rules. These days, text acknowledgement has become a significant develo** zone in handling images and extraction of data. In this proposed work, an efficient text extraction method enhanced artificial neural network-based fuzzy inference system (EANN-FIS) is introduced for relevant text extraction including hidden layers. The proposed method is compared with the traditional methods and the results show that the proposed method is exhibiting better performance in terms of accuracy and speed.
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References
Unar, S., Wang, X., Zhang, C., & Wang, C. (2019). Detected text-based image retrieval approach for textual images. IET Image Processing, 13(3), 515–521. https://doi.org/10.1049/iet-ipr.2018.5277.
Yalniz, I. Z., & Manmatha, R. (2019). Dependence models for searching text in document images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(1), 49–63. https://doi.org/10.1109/tpami.2017.2780108.
Wu, K., & Yu, Y. (2018). Automatic object extraction from images using deep neural networks and the level-set method. IET Image Processing, 12(7), 1131–1141. https://doi.org/10.1049/iet-ipr.2017.1144.
Tsai, C., & Tsai, S. (2018). Simultaneous 3D object recognition and pose estimation based on RGB-D images. IEEE Access, 6, 28859–28869. https://doi.org/10.1109/access.2018.2808225.
Khan, N. H., & Adnan, A. (2018). Urdu optical character recognition systems: Present contributions and future directions. IEEE Access, 6, 46019–46046. https://doi.org/10.1109/access.2018.2865532.
Baran, R., Partila, P., & Wilk, R. (2018, January). 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.
Dai, Y., Huang, Z., Gao, Y., Xu, Y., Chen, K., Guo, J., & Qiu, W. (2018, August). Fused text segmentation networks for multi-oriented scene text detection. In 2018 24th International Conference on Pattern Recognition (ICPR) (pp. 3604–3609). IEEE.
V. R. Maddumala. (2020). Enhanced Morphological Operations for Improving the Pixel Intensity Level. International Journal of Advanced Science and Technology, 29(03), 9191–9201.
Manzyuk, O., Pearlmutter, B. A., Radul, A. A., Rush, D. R., & Siskind, J. M. (2019). Perturbation confusion in forward automatic differentiation of higher-order functions. Journal of Functional Programming, 29.
Yin, X., Zuo, Z., Tian, S., & Liu, C. (2016). Text detection, tracking and recognition in video: A comprehensive survey. IEEE Transactions on Image Processing, 25(6), 2752–2773. https://doi.org/10.1109/tip.2016.2554321.
Sarada, K., & Lakshman Narayana, V. (2020). Improving relevant text extraction accuracy using clustering methods. TEST Engineering and Management, 83, 15212–15219.
Yang, J., Price, B., Cohen, S., Lee, H., & Yang, M. (2016). Object contour detection with a fully Convolutional encoder-decoder network. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:https://doi.org/10.1109/cvpr.2016.28
Sahare, P., & Dhok, S. B. (2016). Review of text extraction algorithms for scene-text and document images. IETE Technical Review, 34(2), 144–164. https://doi.org/10.1080/02564602.2016.1160805.
Narayana, V. L., Sudheer, B. N., Maddumala, V. R., & Anusha, P. (2020). Fuzzy base artificial neural network model for text extraction from images. Journal of Critical Reviews, 7(6), 350–354, doi: https://doi.org/10.31838/jcr.07.06.61.
Raghunandan, K. S., Shivakumara, P., Roy, S., Kumar, G. H., Pal, U., & Lu, T. (2019). Multi-script-Oriented text detection and recognition in video/Scene/Born digital images. IEEE Transactions on Circuits and Systems for Video Technology, 29(4), 1145–1162. https://doi.org/10.1109/tcsvt.2018.2817642.
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Bhyrapuneni, S., Anandan, R. (2021). Relevant Text Extraction Using Enhanced Artificial Neural Network Fuzzy Inference System. In: Peng, SL., Hao, RX., Pal, S. (eds) Proceedings of First International Conference on Mathematical Modeling and Computational Science. Advances in Intelligent Systems and Computing, vol 1292. Springer, Singapore. https://doi.org/10.1007/978-981-33-4389-4_41
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DOI: https://doi.org/10.1007/978-981-33-4389-4_41
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