# Sparse Radial Sampling LBP for Writer Identification

arXiv: https://arxiv.org/abs/1504.06133 · HTML: https://mlsanity.com/paper/1504.06133/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2015.

## Built on by (most cited first, 1 of 1)

- [TextileNet: Towards Zero-shot Text-style Segmentation of Manuscripts](https://mlsanity.com/paper/2607.09299/) (2026)

## Abstract (arXiv)

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.