Off-line handwritten word recognition using HMM with adaptive length Viterbi algorithm

Abstract

In this paper, we have developed a handwritten word recognition scheme based on a single contextual hidden Markov model (HMM) incorporated with an adaptive length Viterbi algorithm. This work attempts to extend our earlier HMM scheme for naturally segmented word recognition to cursive and nonsegmented word recognition. The algorithm pre-segments the script into characters and/or fractions of characters, dynamically selects the optimal segmentation points, determines the word length, and recognizes the word according to the maximum path probability. The HMM is on top of, but independent of, script segmentation and character recognition techniques, and therefore can be further improved by incorporating more refined segmentation and character recognition procedure. The experiments have shown promising results.