Online Writer Identification With Sparse Coding-Based Descriptors

This paper proposes a system to identify the authorship of online handwritten documents. We represent the trace of handwriting of a writer with descriptors that are derived from a set of dictionary atoms obtained in a sparse coding framework. The descriptors for each dictionary atom encode the error while using it alone for reconstruction. The use of sparse representation offers flexibility in describing each of the segmented handwritten sub-strokes of a writer as a combination of more than one atom or prototype. The descriptor is constructed by considering the attributes obtained from sets of histograms extracted at a sub-stroke level. In addition, an entropy-based analysis for the bin size to be used for obtaining the feature sets is proposed. The writer descriptor is evaluated on the paragraph and individual text lines of two publicly available English databases (the IAM and IBM-UB1) and a Chinese database-CASIA. We empirically show that the results obtained are promising when compared with previous works.