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Cited by 517

The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.

Word Spotting and Recognition with Embedded Attributes

Almazan, Jon, Gordo, Albert, Fornes, Alicia, Valveny, Ernest·2014·IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI 10.1109/tpami.2014.2339814
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Abstract

This paper addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.

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Metadata

Authors
Almazan, J., Gordo, A., Fornes, A., & Valveny, E.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2014-12-01
Volume
36
Issue
12
Pages
2552-2566
ISSN
  • 0162-8828 (ISSN-L)
  • 2160-9292
DOI
10.1109/tpami.2014.2339814
OpenAlex
W2053317383
Cited by
517
References
53
FWCI
31.75
Type
journal article
Language
en
Open access
closed

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