Handwritten Word Spotting with Corrected Attributes - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2013

Handwritten Word Spotting with Corrected Attributes


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.
Vignette du fichier
cca.jpg (59.55 Ko) Télécharger le fichier Fichier principal
Vignette du fichier
handwritten_iccv13.pdf (808.62 Ko) Télécharger le fichier
Format : Figure, Image
Origin : Files produced by the author(s)

Dates and versions

hal-00906787 , version 1 (20-11-2013)



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⟩
420 View
589 Download



Gmail Facebook Twitter LinkedIn More