Efficient word image retrieval using fast DTW distance

Dynamic time warping (DTW) is a popular distance measure used for recognition free document image retrieval. However, it has quadratic complexity and hence is computationally expensive for large scale word image retrieval. In this paper, we use a fast approximation to the DTW distance, which makes word retrieval efficient. For a pair of sequences, to compute their DTW distance, we need to find the optimal alignment from all the possible alignments. This is a computationally expensive operation. In this work, we learn a small set of global principal alignments from the training data and avoid the computation of alignments for query images. Thus, our proposed approximation is significantly faster compared to DTW distance, and gives 40 times speed up. We approximate the DTW distance as a sum of multiple weighted Eulidean distances which are known to be amenable to indexing and efficient retrieval. We show the speed up of proposed approximation on George Washington collection and multi-language datasets containing words from English and two Indian languages.