EDNets: Deep Feature Learning for Document Image Classification Based on Multi-view Encoder-Decoder Neural Networks-Reference-Cited by-同舟云学术

EDNets: Deep Feature Learning for Document Image Classification Based on Multi-view Encoder-Decoder Neural Networks

Published:2021 Issue: Volume: Page:318-332
ISSN:0302-9743
Container-title:Document Analysis and Recognition – ICDAR 2021
language:
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Author:

Sellami Akrem^ORCID,Tabbone Salvatore^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-86337-1_22

Reference21 articles.

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2. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Computat. Linguist. 5, 135–146 (2017)

3. Chen, S., He, Y., Sun, J., Naoi, S.: Structured document classification by matching local salient features. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR), pp. 653–656. IEEE (2012)

4. Do, T.H., Ramos Terrades, O., Tabbone, S.: DSD: document sparse-based denoising algorithm. Pattern Anal. Appl. 22(1), 177–186 (2018). https://doi.org/10.1007/s10044-018-0714-3

5. Fesseha, A., Xiong, S., Emiru, E.D., Diallo, M., Dahou, A.: Text classification based on convolutional neural networks and word embedding for low-resource languages: Tigrinya. Information 12(2), 52 (2021)

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