A subspace projection approach to feature extraction: The two-dimensional gabor transform for character recognition

Abstract This paper describes an application of the two-dimensional Gabor wavelets as feature extractors for character recognition with neural networks. Our approach is based on an analysis of the function performed by a single hidden unit in the first layer of a network presented with raw pixel data. This weight function can be approximated by a linear combination of basis functions from a fixed set. We establish the duality between this expansion and feature extraction: the projections of an image onto the same basis set play the role of precalculated features, and they are used as the input to the network. Recognizability of images reconstructed from these projections suggests that the necessary information is preserved by the corresponding feature extraction scheme. In this study, the Gabor wavelets provided the best trade-off between dimensionality reduction and quality of the reconstructed images. A local receptive field (LRF) network was trained on the NIST data base of isolated alphanumeric characters and tested on unseen parts of the same data base. The use of Gabor projections instead of original pixel data resulted in improvement from 86.35% to 89.40% for the lowercase, from 89.40% to 96.44% for the uppercase, and from 98.63% to 99.11% for digits, which corresponds to 22–66% reduction of classification error. This LRF-Gabor network became a part of a unified algorithm used by Eastman Kodak Company that finished in the tight group of leaders at the U.S. Census Bureau/NIST First OCR Systems Competition.