Riesen, Kaspar
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Kaspar
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Riesen, Kaspar
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- PublikationCross-evaluation of graph-based keyword spotting in handwritten historical documents(Springer, 2019) Stauffer, Michael; Maergner, Paul; Fischer, Andreas; Riesen, Kaspar; Conte, Donatello; Ramel, Jean-Yves; Foggia, Pasquale [in: Graph-Based Representations in Pattern Recognition. 12th IAPR-TC-15 International Workshop, GbRPR 2019, Tours, France, June 19-21, 2019. Proceedings]In contrast to statistical representations, graphs offer some inherent advantages when it comes to handwriting representation. That is, graphs are able to adapt their size and structure to the individual handwriting and represent binary relationships that might exist within the handwriting. We observe an increasing number of graph-based keyword spotting frameworks in the last years. In general, keyword spotting allows to retrieve instances of an arbitrary query in documents. It is common practice to optimise keyword spotting frameworks for each document individually, and thus, the overall generalisability remains somehow questionable. In this paper, we focus on this question by conducting a cross-evaluation experiment on four handwritten historical documents. We observe a direct relationship between parameter settings and the actual handwriting. We also propose different ensemble strategies that allow to keep up with individually optimised systems without a priori knowledge of a certain manuscript. Such a system can potentially be applied to new documents without prior optimisation.04B - Beitrag Konferenzschrift
- PublikationOffline signature verification using structural dynamic time warping(IEEE, 2019) Stauffer, Michael; Maergner, Paul; Fischer, Andreas; Ingold, Rolf; Riesen, Kaspar [in: ICDAR 2019. The 15th IAPR International Conference on Document Analysis and Recognition. 20-25 September 2019, Sydney, Australia. Proceedings]In recent years, different approaches for handwriting recognition that are based on graph representations have been proposed (e.g. graph-based keyword spotting or signature verification). This trend is mostly due to the availability of novel fast graph matching algorithms, as well as the inherent flexibility and expressivity of graph data structures when compared to vectorial representations. That is, graphs are able to directly adapt their size and structure to the size and complexity of the respective handwritten entities. However, the vast majority of the proposed approaches match the graphs from a global perspective only. In the present paper, we propose to match the underlying graphs from different local perspectives and combine the resulting assignments by means of Dynamic Time Warping. Moreover, we show that the proposed approach can be readily combined with global matchings. In an experimental evaluation, we employ the novel method in a signature verification scenario on two widely used benchmark datasets. On both datasets, we empirically confirm that the proposed approach outperforms state-of-the-art methods with respect to both accuracy and runtime.04B - Beitrag Konferenzschrift
- PublikationGraph embedding for offline handwritten signature verification(2019) Stauffer, Michael; Maergner, Paul; Fischer, Andreas; Riesen, Kaspar [in: ICBEA 2019. Proceedings of 2019 3rd International Conference on Biometric Engineering and Applications (ICBEA 2019). Stockholm, Sweden, May 29-31, 2019]Due to the high availability and applicability, handwritten signatures are an eminent biometric authentication measure in our life. To mitigate the risk of a potential misuse, automatic signature verification tries to distinguish between genuine and forged signatures. Most of the available signature verification approaches make use of vectorial rather than graph-based representations of the handwriting. This is rather surprising as graphs offer some inherent advantages. Graphs are, for instance, able to directly adapt their size and structure to the size and complexity of the respective handwritten entities. Moreover, several fast graph matching algorithms have been proposed recently that allow to employ graphs also in domains with large amounts of data. The present paper proposes to use different graph embedding approaches in conjunction with a recent graph-based signature verification framework. That is, signature graphs are not directly matched with each other, but first compared with a set of predefined prototype graphs, in order to obtain a dissimilarity representation. In an experimental evaluation, we employ the proposed method on two widely used benchmark datasets. On both datasets, we empirically confirm that the learning-free graph embedding outperforms state-of-the-art methods with respect to both accuracy and runtime.04B - Beitrag Konferenzschrift