An improved online writer identification framework using codebook descriptors. (June 2018) - Venugopal, Vivek; Sundaram, Suresh
Abstract : Research highlights: Codebook descriptors proposed for online writer identification. Features along online trace extracted using gap parameter. Extracted features employed to obtain codebook descriptors. Experiments on several publicly available databases to demonstrate our strategy. Abstract: 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.