Sparse Radial Sampling LBP for Writer Identification

TL;DR

This paper introduces a novel Sparse Radial Sampling LBP method for text-as-texture classification, achieving state-of-the-art writer identification results efficiently without complex preprocessing.

Contribution

It extends Local Binary Patterns to a sparse radial sampling variant tailored for text texture analysis, enabling fast, segmentation-free writer identification.

Findings

Achieved state-of-the-art performance on CVL and ICDAR 2013 datasets.

Method is fast and applicable early in the document analysis pipeline.

Does not require segmentation or binarization.

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.