A set of novel features for writer identification

Authors:
Caroline Hertel;Horst Bunke
Affiliations:
Department of Computer Science, University of Bern, Bern, Switzerland;Department of Computer Science, University of Bern, Bern, Switzerland
Venue:
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Year:
2003

Citing 7
Cited 9

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Abstract

A system for writer identification is described in this paper. It first segments a given page of handwritten text into individual lines and then extracts a set of features from each line. These features are subsequently used in a k-nearest-neighbor classifier that compares the feature vector extracted from a given input text to a number of prototype vectors coming from writers with known identity. The proposed method has been tested on a database holding pages of handwritten text produced by 50 writers. On this database a recognition rate of about 90% has been achieved using a single line of handwritten text as input. The recognition rate is increased to almost 100% if a whole page of text is provided to the system.