title: Visual Saliency Models for Text Detection in Real World.
abstract: This paper evaluates the degree of saliency of texts in natural scenes using visual saliency models. A large scale scene image database with pixel level ground truth is created for this purpose. Using this scene image database and five state-of-the-art models, visual saliency maps that represent the degree of saliency of the objects are calculated. The receiver operating characteristic curve is employed in order to evaluate the saliency of scene texts, which is calculated by visual saliency models. A visualization of the distribution of scene texts and non-texts in the space constructed by three kinds of saliency maps, which are calculated using Itti's visual saliency model with intensity, color and orientation features, is given. This visualization of distribution indicates that text characters are more salient than their non-text neighbors, and can be captured from the background. Therefore, scene texts can be extracted from the scene images. With this in mind, a new visual saliency architecture, named hierarchical visual saliency model, is proposed. Hierarchical visual saliency model is based on Itti's model and consists of two stages. In the first stage, Itti's model is used to calculate the saliency map, and Otsu's global thresholding algorithm is applied to extract the salient region that we are interested in. In the second stage, Itti's model is applied to the salient region to calculate the final saliency map. An experimental evaluation demonstrates that the proposed model outperforms Itti's model in terms of captured scene texts.
authors.affiliation: Department of Advanced Information technology, Kyushu University, Fukuoka, Fukuoka, Japan.
authors.forename: Renwu
authors.initials: R
authors.lastname: Gao
authors.affiliation: Department of Advanced Information technology, Kyushu University, Fukuoka, Fukuoka, Japan.
authors.forename: Seiichi
authors.initials: S
authors.lastname: Uchida
authors.affiliation: German Research Center for Artificial Intelligence, Kaiserslautern, Rhineland-Palatinate, Germany.
authors.forename: Asif
authors.initials: A
authors.lastname: Shahab
authors.affiliation: School of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia.
authors.forename: Faisal
authors.initials: F
authors.lastname: Shafait
authors.affiliation: Department of Advanced Information technology, Kyushu University, Fukuoka, Fukuoka, Japan.
authors.forename: Volkmar
authors.initials: V
authors.lastname: Frinken
mesh_terms: D000465:Algorithms; D003071:Cognition / Q000502:physiology*; D006801:Humans; D008962:Models, Theoretical; D011932:Reading; D014796:Visual Perception / Q000502:physiology*; D014956:Writing