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.

A Multioriented Chinese Keyword Spotter Guided by Text Line Detection Chinese keyword spotting is a challenging task as there is no visual blank for Chinese words. Different from English words which are split naturally by visual blanks, Chinese words are generally split only by semantic information. In this paper, we propose a new Chinese keyword spotter for natural images, which is inspired by Mask RCNN. We propose to predict the keyword masks guided by text line detection. Firstly, proposals of text lines are generated by Faster RCNN; Then, text line masks and keyword masks are predicted by segmentation in the proposals. In this way, the text lines and keywords are predicted in parallel. We create two Chinese keyword datasets based on RCTW17 and ICPR MTWI2018 to verify the effectiveness of our method.