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Conference papers

Handwritten Word Spotting with Corrected Attributes

Jon Almazan 1, * Albert Gordo 2, * Alicia Fornés 1 Ernest Valveny 1
* Corresponding author
2 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : We propose an approach to multi-writer word spotting, where the goal is to find a query word in a dataset comprised of document images. We propose an attributes-based approach that leads to a low-dimensional, fixed-length representation of the word images that is fast to compute and, especially, fast to compare. This approach naturally leads to an unified representation of word images and strings, which seamlessly allows one to indistinctly perform query-by-example, where the query is an image, and query-by-string, where the query is a string. We also propose a calibration scheme to correct the attributes scores based on Canonical Correlation Analysis that greatly improves the results on a challenging dataset. We test our approach on two public datasets showing state-of-the-art results.
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Submitted on : Wednesday, November 20, 2013 - 12:48:15 PM
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Jon Almazan, Albert Gordo, Alicia Fornés, Ernest Valveny. Handwritten Word Spotting with Corrected Attributes. ICCV - IEEE International Conference on Computer Vision, Dec 2013, Sydney, Australia. pp.1017-1024, ⟨10.1109/ICCV.2013.130⟩. ⟨hal-00906787⟩



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