KeyWord Spotting using Siamese Triplet Deep Neural Networks

Deep neural networks has shown great success in computer vision fields by achieving considerable state-of-the-art results and are beginning to arouse big interest in the document analysis community. In this paper, we present a novel siamese deep network of three inputs that allows retrieving the most similar words to a given query. The proposed system follows a query-by-example approach according to a segmentation-based technique and aims to learn suitable representations of handwritten word images, for which a simple Euclidean distance could perform the matching. The results obtained for the George Washington dataset show the potential and the effectiveness of the proposed keyword spotting system.