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—This paper proposes a new approach toward scenery character detection. This is a keypoint-based approach where local features and a saliency map are fully utilized. Local features, such as SIFT and SURF, have been commonly used for computer vision and object pattern recognition problems; however, they have been rarely employed in character recognition and(More)
SUMMARY To handle the variety of scene characters, we propose a cooperative multiple-hypothesis framework which consists of an image operator set module, an Optical Character Recognition (OCR) module and an integration module. Multiple image operators activated by multiple parameters probe suspected character regions. The OCR module is then applied to each(More)
—In this paper we propose a part-based skew estimation method which is more robust to larger varieties of text images, such as camera-captured scene images. Specifically, the skew angle at each local part of the input image is estimated independently by referring the local part of upright character images stored as a database. Then the global skew angle is(More)
—The goal of this research is to understand the true distribution of character patterns. Advances in computer technology for mass storage and digital processing have paved way to process a massive dataset for various pattern recognition problems. If we can represent and analyze the distribution of a large-scale character pattern set directly and understand(More)
—In this paper, we propose a structure learning-based scene character detector which is inspired by the observation that characters have their own inherent structures compared with the background. Graphs are extracted from the thinned binary image to represent the topological line structures of scene contents. Then, a graph classifier, namely gBoost(More)
We propose a novel method for recognizing sequential patterns such as motion trajectory of biological objects (i.e., cells, organelle, protein molecules, etc.), human behavior motion, and meteorological data. In the proposed method, a local classifier is prepared for every point (or timing or frame) and then the whole pattern is recognized by majority(More)
The ambitious goal of this research is to understand the real distribution of character patterns. Ideally, if we can collect all possible character patterns, we can totally understand how they are distributed in the image space. In addition, we also have the perfect character recognizer because we know the correct class for any character image. Of course,(More)