PT Unknown
AU A.Nicolaou
   Andrew Bagdanov
   Marcus Liwicki
   Dimosthenis Karatzas
TI Sparse Radial Sampling LBP for Writer Identification
BT 13th International Conference on Document Analysis and Recognition ICDAR2015
PY 2015
BP 716
EP 720
AB 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.
ER