Author:
Chao Yu,Liu Changsong,Peng Liangrui,Wang Yanwei
Publisher
Springer Nature Switzerland
Reference19 articles.
1. Vafaie, M., Bruns, O., Pilz, N., Waitelonis, J., Sack, H.: Handwritten and printed text identification in historical archival documents. In: Archiving Conference, vol. 19, pp. 15–20. Society for Imaging Science and Technology (2022)
2. Dhar, D., Garain, A., Singh, P.K., Sarkar, R.: Hp_docpres: a method for classifying printed and handwritten texts in doctor’s prescription. Multimedia Tools Appl. 80, 9779–9812 (2021). https://doi.org/10.1007/s11042-020-10151-w
3. Norkute, M., Herger, N., Michalak, L., Mulder, A., Gao, S.: Towards explainable AI: assessing the usefulness and impact of added explainability features in legal document summarization. In: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1–7 (2021)
4. Subramani, N., Matton, A., Greaves, M., Lam, A.: A survey of deep learning approaches for ocr and document understanding. arXiv preprint arXiv:2011.13534 (2020)
5. Huang, L., et al.: EnsExam: a dataset for handwritten text erasure on examination papers. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds.) Document Analysis and Recognition - ICDAR 2023, ICDAR 2023, LNCS, vol. 14189, pp. 470–485. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-41682-8_29