R-PHOC: Segmentation-Free Word Spotting Using CNN
Suman K. Ghosh, Ernest Valveny · 2017
This 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 bounding 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. %For all images in the dataset, we first generate a set of word candidate bounding boxes and then use our R-PHOC network to generate PHOC embeddings for all the bounding boxes using a single forward pass. 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.