Enhancing AlexNet for Arabic Handwritten words Recognition Using Incremental Dropout
Rolla Almodfer, Shengwu Xiong, Mohammed Mudhsh, Pengfei Phil Duan · 2017
Currently, the growth of mobile technologies, lead to a necessity to develop handwritten recognition applications. While the recognition of handwritten Latin and Chinese has been extensively investigated using various techniques, so little works have been done on Arabic handwritten recognition, and none of the existing techniques is accurate enough for practical application. Over the past few years, deeper convolutional neural networks (CNNs) have widely been employed for improving handwritten recognition performance. In this paper, we enhance the popular AlexNet for Arabic Handwritten Words Recognition (HWR). By adopting a dropout regularization, we prevented our system against overfitting problem and reduced the error recognition rate. We also investigated ReLU and tanH activation functions performance in the fully connected layers. Through several settings of experiments using the benchmarking IFN/ENIT Database, we achieved a new state-of-the-art classification accuracy of 92.13% and 92.55%. Lastly, we compared our best result to those of previous state-of-the-art.