Shape-based word spotting in handwritten document images

In this paper, we address the problem of word spotting using a shape-based matching scheme between segmented word images represented by local contour features. As in a typical query-by-example (QBE) paradigm, a user selects an instance of the query word from the collection of interest and a ranked list of images is returned, based on their similarity with the query. This is accomplished in two steps. The query image is firstly aligned with the test image according to a similarity measure defined on their descriptors and then the aligned images are matched through a deformable non-rigid point matching algorithm. Experiments are carried out on historical handwritten text, written in Greek and English, respectively. Moreover, comparisons with other QBE methods show the efficiency of our system as well as its flexibility in adapting to different scripts.