Signature matching using supervised topic models

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 used to cluster shape context features to form term frequency histograms which correspond to a vocabulary for the set of signatures in the gallery. Second, a supervised topic model is used to construct an observation/author correspondence. Finally, the correspondence is used to classify query signatures and return the corresponding author. Two datasets are used to test our algorithm: DS-I Tobacco signature dataset with clean signatures and DS-II UMD dataset with noisy signatures. We demonstrate considerable improvement over state of the art methods.