Sparse Radial Sampling LBP for Writer Identification
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.
Keywords
Cite
@article{arxiv.1504.06133,
title = {Sparse Radial Sampling LBP for Writer Identification},
author = {Anguelos Nicolaou and Andrew D. Bagdanov and Marcus Liwicki and Dimosthenis Karatzas},
journal= {arXiv preprint arXiv:1504.06133},
year = {2015}
}
Comments
Submitted to the 13th International Conference on Document Analysis and Recognition (ICDAR 2015)