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Graph similarity features for hmm-based handwriting recognition in historical documents.
| Content Provider | CiteSeerX |
|---|---|
| Author | Fischer, Andreas Riesen, Kaspar Bunke, Horst |
| Abstract | Abstract—Automatic transcription of historical documents is vital for the creation of digital libraries. In this paper we propose graph similarity features as a novel descriptor for handwriting recognition in historical documents based on Hidden Markov Models.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. Keywords-Handwriting recognition; Hidden Markov models I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Graph Similarity Feature Historical Document Hmm-based Handwriting Recognition Hidden Markov Model Abstract Automatic Transcription Text Image Digital Library Single Word Recognition Well-known Statistical Reference Descriptor Novel Descriptor Structural Descriptor Character Prototype Medieval Parzival Data Set Keywords-handwriting Recognition Structural Graph-based Representation |
| Content Type | Text |
| Resource Type | Article |
