An improved segmentation of online English handwritten text using recurrent neural networks
Cuong Tuan Nguyen, Masaki Nakagawa · 2015
Segmentation of online handwritten text recognition is better to employ the dependency on context of strokes written before and after it. This paper shows an application of Bidirectional Long Short-term Memory recurrent neural networks for segmentation of on-line handwritten English text. The networks allow incorporating long-range context from both forward and backward directions to improve the confident of segmentation over uncertainty. We show that applying the method in the semi-incremental recognition of online handwritten English text reduces up to 62% of waiting time, 50% of processing time. Moreover, recognition rate of the system also improves remarkably by 3 points from 71.7%.