Stauffer, Michael
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Stauffer, Michael
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- PublikationFilters for graph-based keyword spotting in historical handwritten documents(Elsevier, 2020) Stauffer, Michael; Fischer, Andreas; Riesen, Kaspar [in: Pattern Recognition Letters]01A - Beitrag in wissenschaftlicher Zeitschrift
- PublikationGraph-based keyword spotting in historical manuscripts using Hausdorff edit distance(Elsevier, 2019) Ameri, Mohammad Reza; Stauffer, Michael; Riesen, Kaspar; Bui, Tien Dai; Fischer, Andreas; Fischer, Andreas [in: Pattern Recognition Letters]Keyword spotting enables content-based retrieval of scanned historical manuscripts using search terms, which, in turn, facilitates the indexation in digital libraries. Recent approaches include graph-based representations that capture the complex structure of handwriting. However, the high representational power of graphs comes at the cost of high computational complexity for graph matching. In this article, we investigate the potential of Hausdorff edit distance (HED) for keyword spotting. It is an efficient quadratic-time approximation of the graph edit distance. In a comprehensive experimental evaluation with four types of handwriting graphs and four benchmark datasets (George Washington, Parzival, Botany, and Alvermann Konzilsprotokolle), we demonstrate a strong performance of the proposed HED-based method when compared with the state of the art, both, in terms of precision and speed.01A - Beitrag in wissenschaftlicher Zeitschrift
- PublikationKeyword spotting in historical handwritten documents based on graph matching(Elsevier, 2018) Stauffer, Michael; Fischer, Andreas; Riesen, Kaspar [in: Pattern Recognition]In the last decades historical handwritten documents have become increasingly available in digital form. Yet, the accessibility to these documents with respect to browsing and searching remained limited as full automatic transcription is often not possible or not sufficiently accurate. This paper proposes a novel reliable approach for template-based keyword spotting in historical handwritten documents. In particular, our framework makes use of different graph representations for segmented word images and a sophisticated matching procedure. Moreover, we extend our method to a spotting ensemble. In an exhaustive experimental evaluation on four widely used benchmark datasets we show that the proposed approach is able to keep up or even outperform several state-of-the-art methods for template- and learning-based keyword spotting.01A - Beitrag in wissenschaftlicher Zeitschrift