Hermite Filter-Based Texture Analysis with Application to Handwriting Document Indexing

We present a texture analysis approach for texture image indexing based on Gabor-like Hermite filters, which are steered versions of discrete Hermite filters. Hermite filters are the backbone of the Hermite transform, which is a polynomial transform and a good model of the human visual system. Experimental results show that our filters have better performance than Gabor filters. The texture analysis system is then applied to handwriting document indexing. For that doing, handwriting documents are decomposed into local frequencies through the presented filter bank and, using this decomposition, we analyze the visual aspect of handwritings to compute similarity measures. A direct application is the management of document databases, allowing to find documents coming from the same author or to classify documents containing handwritings that have similar visual aspect. The current results are very promising and show that it is possible to characterize handwritten drawings without any a priori graphemes segmentation.