PT Unknown
AU Suman Ghosh
   Ernest Valveny
TI Query by String word spotting based on character bi-gram indexing
BT 13th International Conference on Document Analysis and Recognition ICDAR2015
PY 2015
BP 881
EP 885
DI 10.1109/ICDAR.2015.7333888
AB In this paper we propose a segmentation-free query by string word spotting method. Both the documents and query strings are encoded using a recently proposed word representa- tion that projects images and strings into a common atribute space based on a pyramidal histogram of characters(PHOC). These attribute models are learned using linear SVMs over the Fisher Vector representation of the images along with the PHOC labels of the corresponding strings. In order to search through the whole page, document regions are indexed per character bi- gram using a similar attribute representation. On top of that, we propose an integral image representation of the document using a simplified version of the attribute model for efficient computation. Finally we introduce a re-ranking step in order to boost retrieval performance. We show state-of-the-art results for segmentation-free query by string word spotting in single-writer and multi-writer standard datasets
ER