Efficient Word Image Retrieval Using Earth Movers Distance Embedded to Wavelets Coefficients Domain

Abstract

In this paper we use the Earth Movers Distance (EMD) algorithm to measure similarity between shapes for recognizing and searching Arabic words. We have used the Shape Context and the Angular Radial Partitioning descriptors to evaluate matching and recognizing with EMD. Based on the encouraging results of high accuracy and recall, we follow the low-distortion embedding of the Earth Mover's Distance to map the shapes in the database under the EMD distance, into a normed space of wavelet coefficients as differences of coefficients histograms. The approximate k-nearest neighbors in the database of the embedded shapes are retrieved in sub linear time using a Locality-Sensitive Hashing (LSH) and generate a short list of candidates. This short list of candidates is used in a filter and refine strategy and the exact results are achieved using the original EMD on this short list. We demonstrate our method on the MNIST dataset and the freely available Arabic Printed Text Image (APTI) database. Our method achieves a speedup of 4 orders of magnitude over the exact method, at the cost of only a 2.4% reduction in accuracy.