International Conference on Frontiers in Handwriting Recognition
For handwritten signature verification, signature images are typically represented with fixed-sized feature vectors capturing local and global properties of the handwriting. Graphbased representations offer a promising alternative, as they are flexible in size and model the global structure of the handwriting. However, they are only rarely used for signature verification, which may be due to the high computational complexity involved when matching two graphs. In this paper, we take a closer look at two recently presented structural methods for handwriting analysis, for which efficient matching methods are available: keypoint graphs with approximate graph edit distance and inkball models. Inkball models, in particular, have never been used for signature verification before. We investigate both approaches individually and propose a combined verification system, which demonstrates an excellent performance on the MCYT and GPDS benchmark data sets when compared with the state of the art.
offline signature verification, structural pattern recognition, graph edit distance, inkball models
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This work is licensed under a Creative Commons Attribution 4.0 International License.
“Licensed to Smith College and distributed CC-BY under the Smith College Faculty Open Access Policy.”
Maergner, Paul; Howe, Nicholas; Riesen, Kaspar; Ingold, Rolf; and Fischer, Andreas, "Offline Signature Verification via Structural Methods: Graph Edit Distance and Inkball Models" (2018). Computer Science: Faculty Publications, Smith College, Northampton, MA.