Content-Aware Urdu Handwriting Generation
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-41685-9_27
Reference24 articles.
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2. Wali, A., Hussain, S.: Context-sensitive Shape-substitution in Nastaliq writing system: analysis and formulation. In: Sobh, T. (ed.) Innovations and Advanced Techniques in Computer and Information Sciences and Engineering. Springer, Dordrecht (2007). https://doi.org/10.1007/978-1-4020-6268-1_10
3. Graves, A.: Generating sequences with recurrent neural networks. In: arXiv, preprint arXiv:1308.0850 (2013)
4. Aksan, E., Pece, F., Hilliges, O.: DeepWriting: making digital ink editable via deep generative modeling. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2018)
5. Alonso, E., Moysset, B., Messina, R.: Adversarial generation of handwritten text images conditioned on sequences. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 481–486, Sydney, Australia, IEEE (2019)
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1. ET-Network: A novel efficient transformer deep learning model for automated Urdu handwritten text recognition;PLOS ONE;2024-05-17
2. Data Augmentation for Offline Handwritten Text Recognition: A Systematic Literature Review;SN Computer Science;2024-02-10
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