Hermite and Gabor transforms for noise reduction and handwriting classification in ancient manuscripts

Abstract

In this paper, we propose a biologically inspired, global and segmentation free methodology for manuscript noise reduction and classification. Our method consists of developing well-adapted tools for writing enhancement, background noise, text and drawing separation and handwritten patterns characterization with orientation features. We have used here analysis of handwritten images in the spectral domain by frequency decompositions (Hermite transforms) and Gabor filtering for selective text information extraction. We have tested our approach of writing classification on ancient manuscripts corpus, mainly composed of 18th century authors' documents. The current results are very promising: they show that our biologically inspired methodology can be efficiently used for handwriting analysis without any a priori grapheme segmentation. © Springer-Verlag 2007.