Sparse Radial Sampling LBP for Writer Identification
This provides a fast, single-feature method for writer identification that avoids preprocessing steps like segmentation, benefiting document analysis applications.
The paper tackled writer identification by adapting Sparse Radial Sampling Local Binary Patterns for text-as-texture classification, achieving state-of-the-art performance on CVL and ICDAR 2013 datasets.
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.