RNN Based Uyghur Text Line Recognition and Its Training Strategy

Abstract

Uyghur language is written in a modified Arabic script. Due to its cursive nature and the lack of enough labeled training samples, Uyghur document recognition is still a challenging problem. In this paper, we propose a new Recurrent Neural Network (RNN) based Uyghur text line recognition method combining Gated Recurrent Unit (GRU) and Restricted Boltzmann Machine (RBM) with pretraining mechanism. We also present a novel curriculum learning technique guided by sample distribution information. Experimental results on practical Uyghur printed document image dataset show that the proposed network architecture and training strategy not only achieve better recognition accuracy compared with traditional methods, but can accelerate the training speed as well.