Graph Similarity Features for HMM-Based Handwriting Recognition in Historical Documents

Abstract

Automatic transcription of historical documents is vital for the creation of digital libraries. In this paper we propose graph similarity features as a novel descriptor for handwriting recognition in historical documents based on Hidden Markov Models.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.