Image categorization for improving accessibility to information graphics

Authors:
Jinglun Gao;Rafael E. Carrillo;Kenneth E. Barner
Affiliations:
University of Delaware, Newark, DE, USA;University of Delaware, Newark, DE, USA;University of Delaware, Newark, DE, USA
Venue:
Proceedings of the 12th international ACM SIGACCESS conference on Computers and accessibility
Year:
2010

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Abstract

Information graphics are important visual information in digital media. This paper investigates the accessibility issues associate with the information graphics for visually impaired people. The goal is to provide them with comprehensive numerical information contained in the figures. Towards the goal, we address the practical problems of automatic figure categorization, information extraction and multi-modal presentation scheme. In particular, the system identifies the image class using image processing and machine learning algorithms. With the knowledge of the image class, specific domain features and information are extracted, and then different modalities of presentation are employed based on the need. This paper first proposes the system framework and then focuses on the automated categorization algorithm.