Handwritten country name identification using vector quantisation and hidden Markov model

This paper is a study of keyword recognition using vector quantisation and a hidden Markov model. The purpose is to be able to identify a word holistically. This study considers the problem of identifying a handwritten country name from the 189 different country names registered with the Universal Postal Union. The method divides the words in the last line of the address image into 16/spl times/16 pixel blocks which are fed into a vector quantiser. The VQ outputs are classified using a HMM. Some presorting is carried out based on the letter-length of the word. The results on a set of 415 handwritten country names show the method is 85.3% correct with the majority of errors in estimating the letter-length of the word and distorted VQ output due to sloping and slanted words/letters.