A Comparative Study of Filtering Approaches Applied to Color Archival Document Images

TL;DR

This paper compares various filtering techniques for enhancing color archival document images to improve OCR performance, highlighting their strengths and weaknesses through comprehensive experiments.

Contribution

It provides a detailed comparative analysis of scalar, marginal, vector, and hybrid filtering approaches for color image enhancement in archival documents.

Findings

Vector filtering performs best in noise reduction.

Hybrid filtering offers a good balance between detail preservation and noise removal.

Scalar and marginal filters are less effective on degraded archival images.

Abstract

Current systems used by the Tunisian national archives for the automatic transcription of archival documents are hindered by many issues related to the performance of the optical character recognition (OCR) tools. Indeed, using a classical OCR system to transcribe and index ancient Arabic documents is not a straightforward task due to the idiosyncrasies of this category of documents, such as noise and degradation. Thus, applying an enhancement method or a denoising technique remains an essential prerequisite step to ease the archival document image analysis task. The state-of-the-art methods addressing the use of degraded document image enhancement and denoising are mainly based on applying filters. The most common filtering techniques applied to color images in the literature may be categorized into four approaches: scalar, marginal, vector and hybrid. To provide a set of comprehensive guidelines on the strengths and weaknesses of these filtering approaches, a thorough comparative study is proposed in this article. Numerical experiments are carried out in this study on color archival document images to show and quantify the performance of each assessed filtering approach.