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• W2295185764 abstract "Convolutional neural networks (CNNs) have recently become the state-of-the-art tool for large-scale image classification. In this work we propose the use of activation features from CNNs as local descriptors for writer identification. A global descriptor is then formed by means of GMM supervector encoding, which is further improved by normalization with the KL-Kernel. We evaluate our method on two publicly available datasets: the ICDAR 2013 benchmark database and the CVL dataset. While we perform comparably to the state of the art on CVL, our proposed method yields about 0.21 absolute improvement in terms of $$mathrm {mAP}$$ on the challenging bilingual ICDAR dataset." @default.
• W2295185764 created "2016-06-24" @default.
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• W2295185764 date "2015-01-01" @default.
• W2295185764 modified "2023-10-18" @default.
• W2295185764 title "Offline Writer Identification Using Convolutional Neural Network Activation Features" @default.
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• W2295185764 doi "https://doi.org/10.1007/978-3-319-24947-6_45" @default.
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