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Off-line Writer Identification and Verification Using Gaussian Mixture Models

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Machine Learning in Document Analysis and Recognition

Part of the book series: Studies in Computational Intelligence ((SCI,volume 90))

This chapter presents an off-line, text-independent system for writer identification and verification. At the core of the system are Gaussian Mixture Models (GMMs). GMMs provide a powerful yet simple means of representing the distribution of features extracted from the text lines of a writer. For each writer, a GMM is built and trained on text lines of that writer. In the identification or verification phase, a text line of unknown origin is presented to each of the models. As a result of the recognition process each model returns a log-likelihood score. These scores are used for both the identification and the verification task. Three types of confidence measures are defined on the scores: simple score based, cohort model based, and world model based confidence measures. Experiments demonstrate a very good performance of the system on the identification and the verification task.

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Author information

Authors and Affiliations

  1. Institute of Computer Science and Applied Mathematics (IAM), University of Bern, Neubrückstrasse 10, CH-3012, Bern, Switzerland

    Andreas Schlapbach & Horst Bunke

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  1. Andreas Schlapbach
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  2. Horst Bunke
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Editors and Affiliations

  1. Dipartimento di Sistemi e Informatica, University of Florence, Via S. Marta, 3, 50139, Firenze, Italy

    Simone Marinai

  2. Hitachi Central Research Laboratory, 1-280, Higashi-Koigakubo, Kokubunji-shi, Tokyo, 185-8601, Japan

    Hiromichi Fujisawa

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Schlapbach, A., Bunke, H. (2008). Off-line Writer Identification and Verification Using Gaussian Mixture Models. In: Marinai, S., Fujisawa, H. (eds) Machine Learning in Document Analysis and Recognition. Studies in Computational Intelligence, vol 90. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76280-5_16

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  • DOI: https://doi.org/10.1007/978-3-540-76280-5_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76279-9

  • Online ISBN: 978-3-540-76280-5

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