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Graph Similarity Features for HMM-Based Handwriting Recognition in Historical Documents

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3 Author(s)
Fischer, A. ; Inst. of Comput. Sci. & Appl. Math., Univ. of Bern, Bern, Switzerland ; Riesen, K. ; Bunke, H.

Automatic transcription of historical documents is vital for the creation of digital libraries. In this paper we propose graph similarity features as a novel descriptor for handwriting recognition in historical documents based on Hidden Markov Models. Using a structural graph-based representation of text images, a sequence of graph similarity features is extracted by means of dissimilarity embedding with respect to a set of character prototypes. On the medieval Parzival data set it is demonstrated that the proposed structural descriptor significantly outperforms two well-known statistical reference descriptors for single word recognition.

Published in:

Frontiers in Handwriting Recognition (ICFHR), 2010 International Conference on

Date of Conference:

16-18 Nov. 2010