FEATURE SELECTION FOR HMM AND BLSTM BASED HANDWRITING RECOGNITION OF WHITEBOARD NOTES

Abstract

In this paper, we describe feature selection experiments for online handwriting recognition. We investigated a set of 25 online and pseudo-offline features to find out which features are important and which features may be redundant. To analyze the saliency of the features, we applied a sequential forward and a sequential backward search on the feature set. A hidden Markov model and a neural network based recognizer have been used as recognition engines. In our experiments, we obtained interesting results. Using a set of only five features, we achieved a performance similar to that of the reference system that uses all 25 features. The five selected features have a low correlation and have been the top choices during the first iterations of the forward search with both recognizers. Furthermore, for both recognizers, subsets have been identified that outperform the reference system with statistical significance. In order to assess the results more rigorously, we have compared our recognizer with the widely used commercial recognizer from Microsoft.