Combining Multiple Features for Text-Independent Writer Identification and Verification

In recent years, we proposed a number of new and very effective features for automatic writer identification and verification. They are probability distribution functions (PDFs) extracted from the handwriting images and characterize writer individuality independently of the textual content of the written samples. In this paper, we perform an extensive analysis of feature combinations. In our fusion scheme, the final unique distance between two handwritten samples is computed as the average of the distances due to the individual features participating in the combination. Obtained on a large dataset containing 900 writers, our results show that fusing multiple features (directional, grapheme, run-length PDFs) yields increased writer identification and verification performance.