Preparation of an unconstrained Vietnamese online handwriting database and recognition experiments by recurrent neural networks

Published in In: Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR, 2016

Abstract: This paper presents our attempts to collect and analyze unconstrained Vietnamese online handwriting text patterns by pen-based computers. Totally, our database contains over 120,000 strokes from more than 140,000 characters, which is one of the largest Vietnamese online handwriting pattern databases currently. For building and analyzing our database, we made a collection tool, a line segmentation tool, and a delayed stroke detection tool. Moreover, we investigated some statistical information from personal information of writers. In order to solve the unconstrained handwriting recognition problem, we conducted experiments using Bidirectional Long Short-Term Memory (BLSTM) networks. BLSTM network is architecture of Recurrent Neural Network (RNN) and applied recently for many related problems. The performance of BLSTM network on our database is nearly 80{\%} of accuracy even though this database contains many delayed strokes. In near future, we are going to avail our database for research purposes, as it would be the fundamental for the handwriting recognition research.

Recommended citation: Hung Nguyen, Cuong Nguyen, Pham Bao, Masaki Nakagawa, "Preparation of an unconstrained Vietnamese online handwriting database and recognition experiments by recurrent neural networks." In: Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR, 2016.