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pro vyhledávání: '"Rouhou, Ahmed Cheikh"'
The Transformer has quickly become the dominant architecture for various pattern recognition tasks due to its capacity for long-range representation. However, transformers are data-hungry models and need large datasets for training. In Handwritten Te
Externí odkaz:
http://arxiv.org/abs/2303.13931
CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition
Autor:
Dhiaf, Marwa, Souibgui, Mohamed Ali, Wang, Kai, Liu, Yuyang, Kessentini, Yousri, Fornés, Alicia, Rouhou, Ahmed Cheikh
Self-supervised learning has recently emerged as a strong alternative in document analysis. These approaches are now capable of learning high-quality image representations and overcoming the limitations of supervised methods, which require a large am
Externí odkaz:
http://arxiv.org/abs/2303.09347
Transformer-based approach for joint handwriting and named entity recognition in historical document
Publikováno v:
In Pattern Recognition Letters March 2022 155:128-134
Publikováno v:
2015 13th International Conference on Document Analysis & Recognition (ICDAR); 2015, p836-840, 5p
Autor:
Haddad, Hatem, Rouhou, Ahmed Cheikh, Messaoudi, Abir, Korched, Abir, Fourati, Chayma, Sellami, Amel, Ben HajHmida, Moez, Ghriss, Faten
Publikováno v:
SN Computer Science; March 2023, Vol. 4 Issue: 2