Online Cursive Handwriting Mongolia Words Recognition with Recurrent Neural Networks

This paper primarily discussed Online Handwriting Recognition methods for Mongolia words which being often used among the Mongolia people in the North China. Because of the characteristic of the whole body of the Mongolia words, namely connectivity between the characters, thereby the segmentation of Mongolia words is very difficult. We introduced a recurrent neural network to online handwriting Mongolia words recognition. The system consists of an advanced recurrent neural network with an output layer designed for sequence labelings, partially combined with a probabilistic language model. Experimental results show that unconstrained Mongolia words achieve recognition rates about 80%, compared with about 70% using a previous developed HMM-based recognition system.