An Investigation of Discrete Hidden Markov Models on Handwritten Short Answer Assessment System

Abstract

This paper presents an investigation of an off-line automatic assessment system utilising discrete Hidden Markov Models. A set of geometric features were extracted from handwritten words and were later classified by HMMs. There were two training datasets employed in the experiments; the first training dataset contained all correct answers to the questions whereas another training dataset contained both correct and incorrect answers to the questions. Datasets contained 3,000 and 3,400 handwritten samples, respectively. The experiments yielded promising results whereby the highest recognition rate of 91.90% with a 100% accuracy was achieved on our database.