Word Spotting in Handwritten Text Using Contour-Based Models

In this paper, we propose a method for spotting keywords in images of handwritten text. Relying on an object detection system in real images, local contour features are extracted from segmented word images in order to obtain a representative shape of a word-class. Thus, word spotting is cast following a query-by-word-class scenario where class models are generated using a random subset of the images belonging to that class. Cumbersome multi-writer conditions are tackled with a statistical model of intra-class deformations using principal component analysis (PCA). Novel word instances are detected through a combination of a Hough-style voting scheme with a non-rigid point matching algorithm. Finally, we evaluate the system's performance for word spotting as a classification task, using a vocabulary of word models.