Online Writer Identification using GMM Based Feature Representation and WriterSpecific Weights 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 GMMbased feature representation where in each pointbased 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 sumpooled 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.

Cross multiscale locally encoded gradient patterns for offline textindependent writer identification Abstract   Writer identification is experiencing a revival of activity in recent years and continues to attract great deal of attention as a challenging and important area of research in the field of forensic and authentication. In this work, we introduce a reliable offline system for textindependent writer identification of handwritten documents. Feature engineering is an essential component of a pattern recognition system, which can enhance or decrease the classification performance. A welldesigned and defined feature extraction method improves the classification task. This paper proposes, for feature extraction, an effective, yet highquality and conceptually simple feature image descriptor referred to as Cross multiscale Locally encoded Gradient Patterns (CLGP). The proposed CLGP feature extraction method, which is expected to better represent salient local writing structure, operates at small observation regions (i.e., connected component subimages) of the writing sample. CLGP histogram feature vectors computed from all these observation regions in all writing samples are considered as classification inputs to identify query writers using the Nearest Neighbor Classifier (1NN). Our system is evaluated on six standard databases (IFNENIT, AHTIDMW, CVL, IAM, Firemaker, and ICDAR2011) including handwritten samples in Arabic, English, French, Greek, German, and Dutch languages. Comparing the identification performance with old and recent stateoftheart methods, the proposed system achieves the highest performance on IFNENIT, AHTIDMW, and ICDAR2011 databases, and demonstrates competitive performance on IAM, CVL, and Firemaker databases.