A text reading algorithm for both natural and born-digital images
Identifiers
Permanent link (URI): http://hdl.handle.net/10017/43242DOI: 10.1016/j.imavis.2013.01.003
ISSN: 0262-8856
Publisher
Elsevier
Date
2013-03-01Funders
Ministerio de Economía y Competitividad
Comunidad de Madrid
Bibliographic citation
González, A. & Bergasa, L.M. 2013, "A text reading algorithm for natural images", Image and Vision Computing, vol. 31, no. 3, pp. 255-274.
Keywords
Text detection
Text recognition
Character recognition
Character segmentation
Natural images
Scene text detection
Project
info:eu-repo/grantAgreement/MICINN//TRA2011-29001-C04-01
info:eu-repo/grantAgreement/CAM//S-0505%2FDPI%2F000176
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/acceptedVersion
Publisher's version
https://doi.org/10.1016/j.imavis.2013.01.003Rights
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
© 2013 Elsevier
Access rights
info:eu-repo/semantics/openAccess
Abstract
Reading text in natural images has focused again the attention of many researchers during the last few years due to the increasing availability of cheap image-capturing devices in low-cost products like mobile phones. Therefore, as text can be found on any environment, the applicability of text-reading systems is really extensive. For this purpose, we present in this paper a robust method to read text in natural images. It is composed of two main separated stages. Firstly, text is located in the image using a set of simple and fast-to-compute features highly discriminative between character and non-character objects. They are based on geometric and gradient properties. The second part of the system carries out the recognition of the previously detected text. It uses gradient features to recognize single characters and Dynamic Programming (DP) to correct misspelled words. Experimental results obtained with different challenging datasets show that the proposed system exceeds state-of-the-art performance, both in terms of localization and recognition.
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