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Writer Identification and Verification Using GMM Supervectors

Vincent Christlein, David Bernecker, Florian Honig, Elli Angelopoulou

Abstract. This paper proposes a new system for offline writer identification and writer verification. The proposed method uses GMM supervectors to encode the feature distribution of individual writers. Each supervector originates from an in- dividual GMM which has been adapted from a background model via a maximum-a-posteriori step followed by mixing the new statistics with the background model. We show that this approach improves the TOP-1 accuracy of the current best ranked methods evaluated at the ICDAR-2013 competition dataset from 95.1% [13] to 97.1%, and from 97.9% [11] to 99.2% at the C VL dataset, respectively. Additionally, we compare the GMM supervector encoding with other encoding schemes, namely Fisher vectors and Vectors of Locally Aggregated Descriptors.

Code can be found at: