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...
| Autores: | , |
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| 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 |
| 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. |
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