A fuzzy structural approach to handwritten word recognition

Abstract

In this paper, we present a new off-line word recognition system that is able to recognise unconstrained handwritten words from their grey-scale images, and is based on structural information in the handwritten word. We use Gabor filters to extract oriented features from the words. A 2D fuzzy-word classification system has been developed where the spatial location and shape of the membership functions is derived from the training words. The Gabor filter parameters are estimated from the grey-scale word images enabling the Gabor filter to be automatically tuned to the word image. Our experiments show that the proposed method achieves high recognition rates compared to standard classification methods.

This work is supported in part by an Australian Research Council (ARC) large grant.