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Document Navigability: A Need for Print-Impaired

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Anukriti Kumar, Tanuja Ganu, Saikat Guha

Computer Vision and Pattern Recognition · Submitted 21 Jun 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We apply our technique to citations in scientific documents and find it works well both on born-digital as well as scanned documents.

how it is stated · asserted 1.00 read the record →

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arXiv ID2206.10253 (version 1)
Pith IDF6PYEM77, Pith's permanent record of this paper and its review
CitationAnukriti Kumar, Saikat Guha, Tanuja Ganu. "Document Navigability: A Need for Print-Impaired." arXiv:2206.10253 (2022). https://pith.science/paper/F6PYEM77
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,246 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.74 · human participants 0.09 · controlled comparison 0.04 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.07 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Printed documents continue to be a challenge for blind, low-vision, and other print-disabled (BLV) individuals. background 1.00 0.05 0.03 asserted 1.00
s1 In this paper, we focus on the specific problem of (in-)accessibility of internal references to citations, footnotes, figures, tables and equations. contribution 0.54 0.38 0.04 asserted 0.84
s2 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. background 0.90 0.06 0.04 asserted 1.00
s3 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. method 0.51 0.73 0.12 asserted 0.95
s4 We apply our technique to citations in scientific documents and find it works well both on born-digital as well as scanned documents. result 0.99 0.97 0.59 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.95.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

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.

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