Sparse-parametric writer identification using heterogeneous feature groups

This paper evaluates the performance of edge-based directional probability distributions as features in writer identification in comparison to a number of non-angular features. It is noted that angular features outperform all other features. However, the non-angular features provide additional valuable information. Rank-combination was used to realize a sparse-parametric combination scheme based on nearest-neighbor search. Limitations of the proposed methods pertain to the amount of handwritten material needed in order to obtain reliable distribution estimates. The global features treated in this study are sensitive to major style variation (uppervs lower case), slant, and forged styles, which necessitates the use of other features in realistic forensic writer identification procedures.