Local Binary Pattern for Word Spotting in Handwritten Historical Document

TL;DR

This paper introduces a fast, learning-free word spotting method for handwritten historical documents using Local Binary Pattern features, suitable for degraded images and scenarios lacking annotations.

Contribution

The paper proposes a novel, simple, learning-free approach combining LBP and spatial sampling for efficient word spotting in historical documents.

Findings

Low computational time due to LBP features

Effective in degraded and low-quality manuscripts

Comparable or superior retrieval performance to existing methods

Abstract

Digital libraries store images which can be highly degraded and to index this kind of images we resort to word spot- ting as our information retrieval system. Information retrieval for handwritten document images is more challenging due to the difficulties in complex layout analysis, large variations of writing styles, and degradation or low quality of historical manuscripts. This paper presents a simple innovative learning-free method for word spotting from large scale historical documents combining Local Binary Pattern (LBP) and spatial sampling. This method offers three advantages: firstly, it operates in completely learning free paradigm which is very different from unsupervised learning methods, secondly, the computational time is significantly low because of the LBP features which are very fast to compute, and thirdly, the method can be used in scenarios where annotations are not available. Finally we compare the results of our proposed retrieval method with the other methods in the literature.