NESP: Nonlinear enhancement and selection of plane for optimal segmentation and recognition of scene word images
نویسندگان
چکیده
Program Committee Gady Agam, Illinois Institute of Technology (United States); Elisa H. Barney Smith, Boise State Univ. (United States); William A. Barrett, Brigham Young Univ. (United States); Kathrin Berkner, Ricoh Innovations, Inc. (United States); Hervé Déjean, Xerox Research Ctr. Europe Grenoble (France); Xiaoqing Ding, Tsinghua Univ. (China); David Scott Doermann, Univ. of Maryland, College Park (United States); Oleg D. Golubitsky, Google Waterloo (Canada); Jianying Hu, IBM Thomas J. Watson Research Ctr. (United States); Christopher Kermorvant, A2iA SA (France); Laurence Likforman-Sulem, Telecom ParisTech (France); Xiaofan Lin, A9.com, Inc. (United States); Marcus Liwicki, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (Germany); Daniel P. Lopresti, Lehigh Univ. (United States); Umapada Pal, India Statistical Institute (India); Hiroshi Sako, Hosei Univ. (Japan); Sargur N. Srihari, Univ. at Buffalo (United States); Venkata Subramaniam, IBM India Research Lab. (India); Kazem Taghva, Univ. of Nevada, Las Vegas (United States); George R. Thoma, National Library of Medicine (United States); Christian Viard-Gaudin, Univ. de Nantes (France); Berrin Yanikoglu, Sabanci Univ. (Turkey); Jie Zou, National Library of Medicine (United States)
منابع مشابه
NESP: Nonlinear enhancement and selection of plane for optimal segmentation and recognition of scene word images
In this paper, we report a breakthrough result on the difficult task of segmentation and recognition of coloured text from the word image dataset of ICDAR robust reading competition challenge 2: reading text in scene images. We split the word image into individual colour, gray and lightness planes and enhance the contrast of each of these planes independently by a power-law transform. The discr...
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