PT Unknown
AU Suman Ghosh
   Ernest Valveny
TI R-PHOC: Segmentation-Free Word Spotting using CNN
BT 14th International Conference on Document Analysis and Recognition
PY 2017
DI 10.1109/ICDAR.2017.136
DE Convolutional neural network; Image segmentation; Artificial neural network; Nearest neighbor search
AB 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.
ER