Offline Handwritten Devanagari Word Recognition: A Holistic Approach Based on Directional Chain Code Feature and HMM

A hidden Markov model (HMM) based approach is proposed for recognition of offline handwritten Devanagari words. The histogram of chain-code directions in the image-strips, scanned from left to right by a sliding window, is used as the feature vector. A continuous density HMM is proposed to recognize a word image. In our approach the states of the HMM are not determined a priori, but are determined automatically based on a database of handwritten word images. A handwritten word image is assumed to be a string of several image frame primitives. These are in fact the states of the proposed HMM and are found using a certain mixture distribution. One HMM is constructed for each word. To classify an unknown word image, its class conditional probability for each HMM is computed. The class that gives highest such probability is finally selected.