@InProceedings{SumanGhosh2017,
author="Suman Ghosh
and Ernest Valveny",
title="R-PHOC: Segmentation-Free Word Spotting using CNN",
booktitle="14th International Conference on Document Analysis and Recognition",
year="2017",
optkeywords="Convolutional neural network",
optkeywords="Image segmentation",
optkeywords="Artificial neural network",
optkeywords="Nearest neighbor search",
abstract="arXiv:1707.01294This paper proposes a region based convolutional neural network for segmentation-free word spotting. Our network takes as input an image and a set of word candidate bound- ing boxes and embeds all bounding boxes into an embedding space, where word spotting can be casted as a simple nearest neighbour search between the query representation and each of the candidate bounding boxes. We make use of PHOC embedding as it has previously achieved significant success in segmentation- based word spotting. Word candidates are generated using a simple procedure based on grouping connected components using some spatial constraints. Experiments show that R-PHOC which operates on images directly can improve the current state-of- the-art in the standard GW dataset and performs as good as PHOCNET in some cases designed for segmentation based word spotting.",
optnote="DAG; 600.121",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3079), last updated on Fri, 21 Jan 2022 11:02:58 +0100",
doi="10.1109/ICDAR.2017.136",
file=":http://refbase.cvc.uab.es/files/GhV2017a.pdf:PDF"
}