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In this paper, we present a novel signature matching method based on supervised topic models. Shape Context features are extracted from signature shape contours which capture the local variations in signature properties. We then use the concept of topic models to learn the shape context features which correspond to individual authors. The approach consists of three primary steps. First, K-means is...
This paper presents a new method for writer identification, which emulates the approach taken by forensic document examiners. It combines a novel feature, which uses contour gradients to capture local shape and curvature, with character segmentation to create a pseudo-alphabet for a given handwriting sample. A distance metric is then defined between elements of these alphabets that captures character...
In this paper, we propose a fast large-scale signature matching method based on locality sensitive hashing (LSH). Shape Context features are used to describe the structure of signatures. Two stages of hashing are performed to find the nearest neighbours for query signatures. In the first stage, we use M randomly generated hyper planes to separate shape context feature points into different bins, and...
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