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Cited by 14Assistive Technology in Communication and MobilityDigital Accessibility for DisabilitiesTactile and Sensory Interactions

Understanding How Blind Users Handle Object Recognition Errors: Strategies and Challenges

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Hong, Jonggi, Kacorri, Hernisa·2024·The 26th International ACM SIGACCESS Conference on Computers and Accessibility
DOI 10.1145/3663548.3675635PMC PMC11872236
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Abstract

Object recognition technologies hold the potential to support blind and low-vision people in navigating the world around them. However, the gap between benchmark performances and practical usability remains a significant challenge. This paper presents a study aimed at understanding blind users' interaction with object recognition systems for identifying and avoiding errors. Leveraging a pre-existing object recognition system, URCam, fine-tuned for our experiment, we conducted a user study involving 12 blind and low-vision participants. Through in-depth interviews and hands-on error identification tasks, we gained insights into users' experiences, challenges, and strategies for identifying errors in camera-based assistive technologies and object recognition systems. During interviews, many participants preferred independent error review, while expressing apprehension toward misrecognitions. In the error identification task, participants varied viewpoints, backgrounds, and object sizes in their images to avoid and overcome errors. Even after repeating the task, participants identified only half of the errors, and the proportion of errors identified did not significantly differ from their first attempts. Based on these insights, we offer implications for designing accessible interfaces tailored to the needs of blind and low-vision users in identifying object recognition errors.

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  • Limited prior work has examined how BLV people assess image quality issues with existing VLM tools and perceive their impact on captions.

    “limited prior work has examined how BLV people assess image quality issues with existing VLM tools and perceive their impact on captions [61].”

    From "It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models (2026) · cites this paper as [61]

    Section 4.1 (page unavailable) reports how blind and low-vision participants assessed photo quality, including using generated image descriptions and recognition results. Section 6.1 (page unavailable) says further investigation is needed, but the study primarily examines object-recognition errors; it does not examine how users perceive image-quality issues affecting captions from VLM tools. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • When using a prototype object recognizer to identify common food items, BLV participants were only able to identify half of the object recognition errors, even with successive attempts.

    “when using a prototype object recognizer to identify common food items (e.g., soda, bags of chips, canned foods), BLV participants were only able to identify half of the object recognition errors, even with successive attempts, potentially due to objects' similarity in shape and size [61].”

    From "It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models (2026) · cites this paper as [61]

  • This finding aligns with Hong and Kacorri's findings on overall verification frequency.

    “This aligns with Hong and Kacorri's fi ndings on the overall verification frequency [61].”

    From "It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models (2026) · cites this paper as [61]

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Cited by

Sorted by citations

Authors
Hong, J., & Kacorri, H.
Affiliations
  • Stevens Institute of Technology (US)
  • University of Maryland, College Park (US)
ORCID
Venue
The 26th International ACM SIGACCESS Conference on Computers and Accessibility
Publisher
ACM
Published
2024-10-27
Volume
2024
Pages
1-15
DOI
10.1145/3663548.3675635
PMC
PMC11872236
OpenAlex
W4403576785
Cited by
14
References
7
Citations by year
2026: 6 · 2025: 8
FWCI
12.51
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
  • NIDILRR (90REGE0008)
  • NSF (1816380)
Topics
  • Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
  • Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
Keywords
blind users, low vision, assistive technology, camera-based navigation, user experience, error prevention

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