Online Arabic Handwriting Recognition with Dropout Applied in Deep Recurrent Neural Networks
Rania Maalej, Najiba Tagougui, Monji Kherallah · 2016
Lately, Online Arabic Handwriting Recognition has been gaining more interest because of the advances in technology such as the handwriting capturing devices and impressive mobile computers. And since we always try to improve recognition rates, we propose in this work a new system based on a deep recurrent neural networks on which the dropout technique was applied. Our approach is very practical in sequence modelling due to their recurrent connections, also it can learn intricate relationship between input and output layers because of many non-linear hidden layers. In addition to these contributions, our system is protected against overfitting due to powerful performance of dropout. This proposed system was tested with a large dataset ADAB to show its performance against difficult conditions as the variety of writers, the large vocabulary and diversity of style.