Authorship Clustering using TFIDF weighted WordEmbeddings In this paper, we propose a novel idea to solve the problem of Author Clustering which is introduced in PAN2017 Author Identification task. First, we use word embeddings that brings semantic feature of the words along with the grammatical and syntactic information. Secondly, the documents are represented as the TFIDF weighted sum of the embedding vectors corresponding to each word. A higher TFIDF weight implies that the words have a stronger relationship in the documents in which they appear. Lastly, using hierarchical clustering all those documents written by each author are grouped together. Finally, we compare the proposed technique with the top submissions to PAN2017 author clustering task. Through extensive experimentation, we show that the proposed technique outperforms various stateoftheart models on the given task.

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.