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arXiv · 1504.06133

Sparse Radial Sampling LBP for Writer Identification

Anguelos Nicolaou·Andrew D. Bagdanov·Marcus Liwicki·Dimosthenis Karatzas
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Abstract

In this paper we present the use of Sparse Radial Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features.

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Anguelos Nicolaou, Andrew D. Bagdanov, Marcus Liwicki, Dimosthenis Karatzas. 2015-04-23. Sparse Radial Sampling LBP for Writer Identification. https://arxiv.org/abs/1504.06133

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