An improved online writer identification framework using codebook descriptors

This work proposes a text independent writer identification framework for online handwritten data. We derive a strategy that encodes the sequence of feature vectors extracted at sample points of the temporal trace with descriptors obtained from a codebook. The derived descriptors take into account, the scores of each of the attributes in a feature vector, that are computed with regards of the proximity to their corresponding values in the assigned codevector of the codebook. A codebook comprises a set of codevectors that are pre-learnt by a k-means algorithm applied on feature vectors of handwritten documents pooled from several writers. In addition, for constructing the codebook, we consider features that are derived by incorporating a so called 'gap parameter' that captures characteristics of sample points in the neighborhood of the point under consideration. We formulate our strategy in a way that, for a given codebook size k, we employ the descriptors of only k−1 codevectors to construct the final descriptor by concatenation. The usefulness of the descriptor is demonstrated by several experiments that are reported on publicly available databases.