ACCSAMS: Automatic Conversion of Exam Documents to Accessible Learning Material for Blind and Visually Impaired

TL;DR

ACCSAMS is a semi-automatic system that improves exam document accessibility for blind and visually impaired students by restructuring layouts, adding navigation, and providing alternative text, supported by a multilingual annotated dataset.

Contribution

The paper introduces ACCSAMS, a novel semi-automatic system for enhancing exam document accessibility and provides the first multilingual annotated dataset for training deep learning models.

Findings

Successfully creates accessible layouts and removes white space

Adds navigational structures to exam documents

Incorporates alternative text for visual elements

Abstract

Exam documents are essential educational materials for exam preparation. However, they pose a significant academic barrier for blind and visually impaired students, as they are often created without accessibility considerations. Typically, these documents are incompatible with screen readers, contain excessive white space, and lack alternative text for visual elements. This situation frequently requires intervention by experienced sighted individuals to modify the format and content for accessibility. We propose ACCSAMS, a semi-automatic system designed to enhance the accessibility of exam documents. Our system offers three key contributions: (1) creating an accessible layout and removing unnecessary white space, (2) adding navigational structures, and (3) incorporating alternative text for visual elements that were previously missing. Additionally, we present the first multilingual manually annotated dataset, comprising 1,293 German and 900 English exam documents which could serve as a good training source for deep learning models.