Classification of Text and Non-text Components Present in Offline Unconstrained Handwritten Documents Using Convolutional Neural Network
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Springer Nature Switzerland
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https://link.springer.com/content/pdf/10.1007/978-3-031-48876-4_4
Reference12 articles.
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3. Ghosh, S., Hassan, S.K., Khan, A.H., Manna, A., Bhowmik, S., Sarkar, R.: Application of texture-based features for text non-text classification in printed document images with novel feature selection algorithm. Soft. Comput. 26(2), 891–909 (2022)
4. Oyedotun, O.K., Khashman, A.: Document segmentation using textural features summarization and feedforward neural network. Appl. Intell., 1–15 (2016)
5. Sah, A.K., Bhowmik, S., Malakar, S., Sarkar, R., Kavallieratou, E., Vasilopoulos, N.: Text and non-Text recognition using modified HOG descriptor. In: 2017 IEEE Calcutta Conference, CALCON 2017 - Proceedings, 2018, vol. 2018-Janua, pp. 64–68. https://doi.org/10.1109/CALCON.2017.8280697
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1. DSANet: dilated spatial attention network for the detection of text, non-text and touching components in unconstrained handwritten documents;Neural Computing and Applications;2024-06-04
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