The A2iA Arabic Handwritten Text Recognition System at the Open HaRT2013 Evaluation
By Théodore Bluche, Jérôme Louradour, Maxime Knibbe, Bastien Moysset, Mohamed Benzeghiba, Christopher Kermorvant. 2014
48 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Christopher Kermorvant
- Jérôme Louradour
- Vu Tuan Dat Pham
- Yoav Goldberg
- Yoshua Bengio
- Ilya Sutskever
- Yarin Gal
- Graham Neubig
In Computer Vision and Pattern Recognition
- Christopher Kermorvant
- Jérôme Louradour
- Vu Tuan Dat Pham
- Yarin Gal
- Yoav Goldberg
- Ilya Sutskever
- J. Urgen Schmidhuber
- Trevor Cohn
Citations in the corpus
- Dropout Improves Recurrent Neural Networks for Handwriting Recognition (2013, methodology): “In fact, this architecture was featured in our winning entry of the Arabic handwriting recognition competition OpenHaRT 2013 [11], where such a RNN was used as the optical model in the recognition system.”
- My First Deep Learning System of 1991 + Deep Learning Timeline 1962-2013 (2013, unknown): “CTC-LSTM also helped to score first at NIST’s OpenHaRT2013 evaluation [11].”