Online Writer Identification using GMM Based Feature Representation and Writer-Specific Weights
Vivek Venugopal, Suresh Sundaram · 2019
This paper focuses on a method to ascertain the identity of an online handwritten document. The proposed methodology makes use of a set of descriptors that are derived from features obtained in a probabilistic sense. In this regard, we employ a GMM-based feature representation where in each point-based feature vector in the online trace is represented by a vector. Each element of the aforementioned vector quantify the membership to a particular Gaussian in the GMM. A differing aspect is in the proposal of a weighting scheme that measures the influence of each Gaussian of a writer in the probabilistic space. For deriving these weights, we rely on the information obtained from a histogram, by formulating a function of the sum-pooled posterior probabilities obtained across all the enrolled documents in the database. The identification is performed by an ensemble of SVMs where each SVM is modelled for a given writer. The experiments are performed on the publicly available IAM Online handwriting database and the results are competitive with respect to prior works in literature.