Browsing by Author "Shumba, Sandura"

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    Offline writer authentication
    (Stellenbosch : Stellenbosch University, 2021-12) Shumba, Sandura; Coetzer, Johannes; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Applied Mathematics.
    ENGLISH ABSTRACT: In this thesis a number of systems are proposed for the purpose of offline writer authentication. A text-dependent approach is adopted, since a very specific targeted handwritten word is considered for authentication purposes. Feature extraction is facilitated by calculating a number of projections of the targeted word from different angles. Two distinct categories of systems are proposed. The first category employs template matching and is based on the computation of the Euclidean distance and a dynamic time warping (DTW) distance between corresponding feature vectors, while the second category relies on machine learning techniques, that is support vector machines (SVMs) and quadratic discriminant analysis (QDA). Within the context of the proposed machine learning-based systems, a writer-independent protocol is followed. This is achieved by employing a DTW-based dichotomy transformation which converts a feature set in feature space into a dissimilarity vector-based representation in dissimilarity space. This dichotomy transformation is followed by writer-specific dissimilarity vector normalisation which significantly improves interclass separability. The DTW-based dichotomy transformation and writer-specific dissimilarity vector normalisation are novel within the context of offline writer authentication. The systems developed in this study are evaluated on a subset of the CEDAR-LETTER data set. It is demonstrated that the proficiency of the systems developed in this study are at least on par when compared to existing systems. The most proficient SVM-based system developed in this study achieves an AUC of 93% and an equal error rate (EER) of 14.93%.