Radial Line Fourier Descriptor for Historical Handwritten Text Representation

TL;DR

This paper proposes a Radial Line Fourier descriptor for representing handwritten words, improving retrieval in degraded historical manuscripts by being noise-tolerant and compact, and demonstrates its effectiveness in segmentation-free word spotting.

Contribution

Introduction of a novel Radial Line Fourier descriptor for handwritten word representation, enabling efficient, noise-robust, segmentation-free word spotting without training.

Findings

RLF descriptor is effective for noisy, degraded documents

Achieves high accuracy in word spotting tasks

Uses a compact 32-dimensional feature vector

Abstract

Automatic recognition of historical handwritten manuscripts is a daunting task due to paper degradation over time. Recognition-free retrieval or word spotting is popularly used for information retrieval and digitization of the historical handwritten documents. However, the performance of word spotting algorithms depends heavily on feature detection and representation methods. Although there exist popular feature descriptors such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF), the invariant properties of these descriptors amplify the noise in the degraded document images, rendering them more sensitive to noise and complex characteristics of historical manuscripts. Therefore, an efficient and relaxed feature descriptor is required as handwritten words across different documents are indeed similar, but not identical. This paper introduces a Radial Line Fourier (RLF) descriptor for handwritten word representation, with a short feature vector of 32 dimensions. A segmentation-free and training-free handwritten word spotting method is studied herein that relies on the proposed RLF descriptor, takes into account different keypoint representations and uses a simple preconditioner-based feature matching algorithm. The effectiveness of the RLF descriptor for segmentation-free handwritten word spotting is empirically evaluated on well-known historical handwritten datasets using standard evaluation measures.