Unconstrained Handwritten Word Recognition Based on Trigrams Using BLSTM

To get high recognition accuracy, we should train the recognizer with sufficient training data to capture characteristics of various handwriting styles and all possible occurring words. However, in most of the cases, available training data are not satisfactory and enough, especially for unseen data. In this paper, we try to improve the recognition accuracy for unseen data with randomly selected training data, by splitting the training data into two parts based on trigrams and training two recognizers separately. We also propose a modified version of token passing algorithm, which makes use of the outputs of the two recognizers to improve the recognition accuracy.