Hybrid Off-Line Cursive Handwriting Word Recognition

In this paper, we present an off-line cursive word handwriting recognition methodology. This is based on an additive fusion resulted after a novel combination of two different modes of word image normalization and robust hybrid feature extraction. We employ two types of features in a hybrid fashion. The first one, divides the word image into a set of zones and calculates the density of the character pixels in each zone. In the second type of features, we calculate the area that is formed from the projections of the upper and lower profile of the word. The performance of the proposed methodology is demonstrated after testing with the reference IAM cursive handwriting database