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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Keywords
- Feature Vector
- Receiver Operator Characteristic Curve
- Hide Markov Model
- Gaussian Mixture Model
- Text Line
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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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