  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.
