IEEE Trans Image Process - Localizing text in scene images by boundary clustering, stroke segmentation, and string fragment classification.

Tópicos

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Resumo

In this paper, we propose a novel framework to extract text regions from scene images with complex backgrounds and multiple text appearances. This framework consists of three main steps: boundary clustering (BC), stroke segmentation, and string fragment classification. In BC, we propose a new bigram-color-uniformity-based method to model both text and attachment surface, and cluster edge pixels based on color pairs and spatial positions into boundary layers. Then, stroke segmentation is performed at each boundary layer by color assignment to extract character candidates. We propose two algorithms to combine the structural analysis of text stroke with color assignment and filter out background interferences. Further, we design a robust string fragment classification based on Gabor-based text features. The features are obtained from feature maps of gradient, stroke distribution, and stroke width. The proposed framework of text localization is evaluated on scene images, born-digital images, broadcast video images, and images of handheld objects captured by blind persons. Experimental results on respective datasets demonstrate that the framework outperforms state-of-the-art localization algorithms.

Resumo Limpo

paper propos novel framework extract text region scene imag complex background multipl text appear framework consist three main step boundari cluster bc stroke segment string fragment classif bc propos new bigramcoloruniformitybas method model text attach surfac cluster edg pixel base color pair spatial posit boundari layer stroke segment perform boundari layer color assign extract charact candid propos two algorithm combin structur analysi text stroke color assign filter background interfer design robust string fragment classif base gaborbas text featur featur obtain featur map gradient stroke distribut stroke width propos framework text local evalu scene imag borndigit imag broadcast video imag imag handheld object captur blind person experiment result respect dataset demonstr framework outperform stateoftheart local algorithm

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