Efficient Word Recognition Using a Pixel-Based Dissimilarity Measure

In this paper, we propose a word recognition methodology based on a novel size-normalization and a pixel-based image dissimilarity measure. As a first step, we apply a new size-normalization technique using baseline estimation. Starting from those size-normalized images, the difference between two word images is calculated using an image dissimilarity measure based on curvature estimation using integral invariants and a windowed Hausorff distance. We conducted several experiments comparing the new methodology with state-of-the-art techniques using ground truth data from a historical book. The experiments prove the efficiency of the proposed size normalization as well as of the overall proposed sytem.