%0 Conference Proceedings
%T A Sliding Window Framework for Word Spotting Based on Word Attributes
%A Suman Ghosh
%A Ernest Valveny
%B Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015
%D 2015
%V 9117
%I Springer International Publishing
%@ 0302-9743
%@ 978-3-319-19389-2
%F Suman Ghosh2015
%O DAG; 600.077
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2716), last updated on Thu, 12 May 2016 13:47:43 +0200
%X In this paper we propose a segmentation-free approach to word spotting. Word images are first encoded into feature vectors using Fisher Vector. Then, these feature vectors are used together with pyramidal histogram of characters labels (PHOC) to learn SVM-based attribute models. Documents are represented by these PHOC based word attributes. To efficiently compute the word attributes over a sliding window, we propose to use an integral image representation of the document using a simplified version of the attribute model. Finally we re-rank the top word candidates using the more discriminative full version of the word attributes. We show state-of-the-art results for segmentation-free query-by-example word spotting in single-writer and multi-writer standard datasets.
%K Word spotting
%K Sliding window
%K Word attributes
%U http://refbase.cvc.uab.es/files/GhV2015b.pdf
%U http://dx.doi.org/10.1007/978-3-319-19390-8_73
%P 652-661