Constrained and Parametric Dynamic Programming for Word Image Retrieval

Abstract

There have been many published works on using Dynamic Time Warping (DTW) for word image matching. It has also been shown that constrained DTW works better than classical DTW, due to it's property to limit the warping path and to avoid pathological matching. Moreover, some studies also demonstrate that variants of DTW can perform better, compared to classical DTW. To take advantages from these techniques, we propose in this paper to study how combination of such approaches can even more improve the results. First, we propose to use pseudo Local DTW (LDTW: more efficient than DTW in our experiments) with a constraint band. Next, several other sequence matching techniques are parametrically combined to study their potential advantages. For this, we adapted the approach to the context of word image matching to obtain the value of the parameters. It is shown that, at least on Bentham and George Washington datasets, results can be improved by combining some algorithms. Please note that the goal of this research work is not to propose a competitive word spotting technique but to propose a dynamic programming based learning free approach for word spotting or image matching, which can perform moderately with respect to other existing approaches in this domain.