Query by String word spotting based on character bi-gram indexing
This addresses the problem of efficient word retrieval in document images for researchers and practitioners in document analysis, though it is incremental as it builds on existing PHOC and Fisher Vector methods.
The paper tackles segmentation-free query by string word spotting by encoding documents and queries into a common attribute space using PHOC and Fisher Vectors with linear SVMs, achieving state-of-the-art results on standard datasets.
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