Holistic handwritten word recognition using temporal features derived from off-line images

Abstract

This paper describes an algorithm for holistic recognition of off-line cursive words using temporal stroke information derived from off-line script. Temporal information is extracted by traversing the strokes without explicit segmentation of the word into constituent characters. The word image is then mapped on to a feature vector matrix of uptrends and downtrends of strokes. This feature vector matrix is compared to prestored feature vector of lexicon entries and ranked accordingly. On a test set of images, the temporal feature extraction rate is 80%. Given the correct set of temporal features, the recognition rate of the holistic classifier is 81% on small lexicons.