Can One Deep Learning Model Learn ScriptIndependent Multilingual WordSpotting? Word spotting has gained increased attention lately as it can be used to extract textual information from handwritten documents and scenetext images. Current word spotting approaches are designed to work on a single language andor script. Building intelligent models that learn scriptindependent multilingual wordspotting is challenging due to the large variability of multilingual alphabets and symbols. We used ResNet152 and the Pyramidal Histogram of Characters (PHOC) embedding to build a onemodel scriptindependent multilingual wordspotting and we tested it on Latin, Arabic, and Bangla (Indian) languages. The onemodel we propose performs on par with the multimodel languagespecific wordspotting system, and thus, reduces the number of models needed for each script andor language.

KeyWord Spotting using Siamese Triplet Deep Neural Networks Deep neural networks has shown great success in computer vision fields by achieving considerable stateoftheart 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 querybyexample approach according to a segmentationbased 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.