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Related Concept Videos

Visual Agnosia01:12

Visual Agnosia

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Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
176

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Addressing visual impairments: Essential software requirements for image caption solutions.

Rosalvo Ferreira de Oliveira Neto1, Larissa Almeida Rocha1, Milton Pereira de Carvalho Filho2

  • 1Computer Engineering Department, Federal University of Vale do São Francisco, Juazeiro -Ba, Brazil.

Assistive Technology : the Official Journal of RESNA
|October 30, 2024
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Deep learning image captioning can improve screen readers for visually impaired users. Current tools need enhancement in describing individuals, object colors, and image context for better digital accessibility.

Keywords:
Artificial intelligenceassistive technologydigital accessibilityimage captioningvisually impaired

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Assistive Technology

Background:

  • Visually impaired individuals rely on assistive technologies like screen readers for digital device interaction.
  • Advancements in deep learning and image captioning offer potential for enhanced audio descriptions.

Purpose of the Study:

  • To identify critical software requirements for image captioning tools tailored to visually impaired users.
  • To evaluate the effectiveness of existing deep learning models against these requirements.

Main Methods:

  • Qualitative research involving an online survey of visually impaired users' preferences for audio descriptive software.
  • Evaluation of current deep learning image captioning models' capabilities.

Main Results:

  • User preferences highlighted the need for detailed descriptions of individuals, object colors, and image context.
  • Existing deep learning captioning models demonstrate limitations in meeting these specific user-defined requirements.
  • Comprehensive image data is crucial for effective captioning.

Conclusions:

  • Current image captioning tools are not fully optimized for the visually impaired.
  • There is a significant need for improved image captioning systems to advance digital accessibility.
  • Future research should focus on developing models that incorporate user-defined requirements for richer image descriptions.