Stauffer, MichaelFischer, AndreasRiesen, Kaspar2017-09-222016-11Stauffer M., Fischer A., Riesen K. (2016) Graph-Based Keyword Spotting in Historical Handwritten Documents. In: Robles-Kelly A., Loog M., Biggio B., Escolano F., Wilson R. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2016. Lecture Notes in Computer Science, vol 10029. Springer, Cham.0302-9743 (print)1611-3349 (online)10.1007/978-3-319-49055-7_50http://hdl.handle.net/2263/62502Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR). S+SSPR 2016: Structural, Syntactic, and Statistical Pattern Recognition pp. 564-573.The amount of handwritten documents that is digitally available is rapidly increasing. However, we observe a certain lack of accessibility to these documents especially with respect to searching and browsing. This paper aims at closing this gap by means of a novel method for keyword spotting in ancient handwritten documents. The proposed system relies on a keypoint-based graph representation for individual words. Keypoints are characteristic points in a word image that are represented by nodes, while edges are employed to represent strokes between two keypoints. The basic task of keyword spotting is then conducted by a recent approximation algorithm for graph edit distance. The novel framework for graph-based keyword spotting is tested on the George Washington dataset on which a state-of-the-art reference system is clearly outperformed.en© Springer International Publishing AG 2016. The original publication is available at : http://link.springer.combookseries/558.Handwritten keyword spottingBipartite graph matchingGraph representation for wordsPostprint Article