End-to-End Model Based on Bidirectional LSTM and CTC for Online Handwritten Mongolian Word Recognition

Abstract

An end-to-end model for Traditional Mongolian online handwritten word recognition is proposed in this paper. According to the characteristics of input and output data, the proposed model consists of a bidirectional Long Short-Term Memory(LSTM) network and a Connectionist Temporal Classification(CTC) network. Bidirectional LSTM network is the core of the model, and the CTC network is added to LSTM network. The key step of this research is to switch from the LSTM network output to the conditional probability distribution on the label sequence through the CTC layer. Therefore, for each given input sequence, the model completes the recognition task by choosing the most possible label. In addition, There is not many researchs on online handwritten Mongolian recognition. Therefore, in this study, we will also focus on recognizing wrong labels, finding out the types of errors, and analyzing the possible causes of errors.