A text reading algorithm for both natural and born-digital images

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-read...

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Detalles Bibliográficos
Autores: González Arroyo, Álvaro, Bergasa Pascual, Luis Miguel|||0000-0002-0087-3077
Tipo de recurso: artículo
Fecha de publicación:2013
País:España
Institución:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/43242
Acceso en línea:http://hdl.handle.net/10017/43242
https://dx.doi.org/10.1016/j.imavis.2013.01.003
Access Level:acceso abierto
Palabra clave:Text detection
Text recognition
Character recognition
Character segmentation
Natural images
Scene text detection
Electrónica
Electronics
Descripción
Sumario: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.