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Titel
Learning Features for Writer Retrieval and Identification using Triplet CNNs
Verfasser / Verfasserin Keglevic, Manuel ; Fiel, Stefan ; Sablatnig, Robert
Erschienen in
16th International Conference on Frontiers in Handwriting Recognition (ICFHR 2018), Niagara Falls, New York, USA, 2018, S. 211-216
Erschienen2018
SpracheEnglisch
DokumenttypAufsatz in einem Sammelwerk
Schlagwörter (EN)Writer Identification / Writer Retrieval / Document Analysis
Projekt-/ReportnummerEuropean Union's Horizon 2020: 674943
ISBN9781538658758
URNurn:nbn:at:at-ubtuw:3-3767 Persistent Identifier (URN)
DOI10.1109/ICFHR-2018.2018.00045 
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Learning Features for Writer Retrieval and Identification using Triplet CNNs [1.75 mb]
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This paper presents a method for writer retrieval and identification using a feature descriptor learned by a Convolutional Neural Network. Instead of using a network for classification, we propose the use of a triplet network that learns a similarity measure for image patches. Patches of the handwriting are extracted and mapped into an embedding where this similarity measure is defined by the L2 distance. The triplet network is trained by maximizing the interclass distance, while minimizing the intraclass distance in this embedding. The image patches are encoded using the learned feature descriptor. By applying the Vector of Locally Aggregated Descriptors encoding to these features, we generate a feature vector for each document image. A detailed parameter evaluation is given which shows that this method achieves a mean average precision of 86.1% on the ICDAR 2013 writer identification dataset, but future work has to be done to improve the performance on historic datasets. In addition, the strategy for clustering the feature space is investigated.

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