CVHCJun 21, 2022

Document Navigability: A Need for Print-Impaired

arXiv:2206.10253v1h-index: 12
Originality Synthesis-oriented
AI Analysis

This addresses a specific accessibility challenge for print-impaired individuals, though it is incremental as it builds on existing vision-based methods for document analysis.

The paper tackles the problem of inaccessibility of internal references (citations, footnotes, figures, tables, equations) in printed documents for blind, low-vision, and print-disabled individuals, proposing a vision-based technique to locate referenced content and extract metadata, which works well on both born-digital and scanned scientific documents.

Printed documents continue to be a challenge for blind, low-vision, and other print-disabled (BLV) individuals. In this paper, we focus on the specific problem of (in-)accessibility of internal references to citations, footnotes, figures, tables and equations. While sighted users can flip to the referenced content and flip back in seconds, linear audio narration that BLV individuals rely on makes following these references extremely hard. We propose a vision based technique to locate the referenced content and extract metadata needed to (in subsequent work) inline a content summary into the audio narration. We apply our technique to citations in scientific documents and find it works well both on born-digital as well as scanned documents.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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