GAN-based text line segmentation method for challenging handwritten documents
Author:
Funder
Süleyman Demirel Üniversitesi
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10032-024-00488-5.pdf
Reference33 articles.
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2. Ataer, E., Duygulu, P.: Retrieval of ottoman documents. In: Proceedings of the 8th ACM International Workshop on Multimedia Information Retrieval, pp. 155–162 (2006)
3. Barakat, B., Droby, A., Kassis, M., El-Sana, J.: Text line segmentation for challenging handwritten document images using fully convolutional network. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 374–379. IEEE (2018)
4. Barakat, B.K., Droby, A., Alaasam, R., Madi, B., Rabaev, I., El-Sana, J.: Text line extraction using fully convolutional network and energy minimization. In: Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021, Proceedings, Part VII, pp. 126–140. Springer, Berlin (2021)
5. Barakat, B.K., Droby, A., Alaasam, R., Madi, B., Rabaev, I., Shammes, R., El-Sana, J.: Unsupervised deep learning for text line segmentation. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 2304–2311. IEEE (2021)
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