Information Extraction from Visually Rich Documents Using Directed Weighted Graph Neural Network
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-70552-6_15
Reference22 articles.
1. Belhadj, D., Belaïd, A., Belaïd, Y.: Improving information extraction from semi-structured documents using attention based semi-variational graph auto-encoder. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds.) Document Analysis and Recognition - ICDAR 2023, ICDAR 2023, LNCS, vol. 14188, pp. 113–129. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-41679-8_7
2. Lecture Notes in Computer Science;D Belhadj,2021
3. Chen, Y.M., Hou, X.T., Lou, D.F., Liao, Z.L., Liu, C.L.: DAMGCN: entity linking in visually rich documents with dependency-aware multimodal graph convolutional network. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds.) Document Analysis and Recognition - ICDAR 2023, ICDAR 2023, LNCS, vol. 14189, pp. 33–47. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-41682-8_3
4. Gbada, H., Kalti, K., Mahjoub, M.: Multimodal weighted graph representation for information extraction from visually rich documents. Neurocomputing 573, 127223 (2024). https://doi.org/10.1016/j.neucom.2023.127223
5. Harley, A.W., Ufkes, A., Derpanis, K.G.: Evaluation of deep convolutional nets for document image classification and retrieval. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 991–995. IEEE (2015)
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