BrowseWithMe: An Online Clothes Shopping Assistant for People with Visual Impairments
Open accessAbstract
Our interviews with people who have visual impairments show clothes shopping is an important activity in their lives. Unfortunately, clothes shopping web sites remain largely inaccessible. We propose design recommendations to address online accessibility issues reported by visually impaired study participants and an implementation, which we call BrowseWithMe, to address these issues. BrowseWithMe employs artificial intelligence to automatically convert a product web page into a structured representation that enables a user to interactively ask the BrowseWithMe system what the user wants to learn about a product (e.g., What is the price? Can I see a magnified image of the pants?). This enables people to be active solicitors of the specific information they are seeking rather than passive listeners of unparsed information. Experiments demonstrate BrowseWithMe can make online clothes shopping more accessible and produce accurate image descriptions.
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People who are blind or have low vision rely on the Internet to access information and perform everyday tasks, such as shopping for essential items and managing personal finances.
“People who are blind or have low vision rely on the Internet to access information and perform everyday tasks, such as shopping for essential items and managing personal finances [46].”
From From Cluttered to Clear: Improving the Web Accessibility Design for Screen Reader Users in E-commerce With Generative AI (2025) · cites this paper as [46]
The Introduction and Obtaining Product Information sections discuss online shopping and using websites to obtain product information for people who are blind or have low vision. The paper focuses on clothes shopping; it does not support the broader examples of shopping for essential items or managing personal finances. Page numbers are unavailable in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measuredExisting solutions primarily target specific elements rather than ensuring a comprehensive, end-to-end accessible experience across the entire web page.
“However, these existing solutions primarily target specific elements, such as product descriptions or image retrieval, rather than ensuring a comprehensive, end-to-end accessible experience across the entire web page, particularly for users who may be unfamiliar with a new platform.”
From From Cluttered to Clear: Improving the Web Accessibility Design for Screen Reader Users in E-commerce With Generative AI (2025) · cites this paper as [46]
Inadequate descriptions of images and unparsed text information significantly reduce BLV consumers' engagement with online shopping websites.
“Hence, inadequate descriptions of images and unparsed text information significantly reduce BLV consumers' engagement with online shopping websites [30, 31].”
From Revamp: Enhancing Accessible Information Seeking Experience of Online Shopping for Blind or Low Vision Users (2021) · cites this paper as [31]
Online reviews have been mentioned as a useful resource to assist BLV customers' desire for more product information.
“Online reviews have been mentioned as an useful resource to assist BLV customers' desire for more product information [16, 30, 31], such as supporting question-answering[20, 36] and generating summaries [12, 15].”
From Revamp: Enhancing Accessible Information Seeking Experience of Online Shopping for Blind or Low Vision Users (2021) · cites this paper as [31]
BLV users face significant barriers on online shopping websites due to inadequate image descriptions and screen readers' inability to filter out a large amount of information on a product page.
“However, prior studies [30, 31] show that BLV people still face significant barriers on online shopping websites due to inadequate image descriptions and screen readers' inability to filter out a large amount of information on a product page.”
From Revamp: Enhancing Accessible Information Seeking Experience of Online Shopping for Blind or Low Vision Users (2021) · cites this paper as [31]
Two main challenges mentioned by our participants are lack of visual information and lack of efficient ways to navigate through information.
“Aligned with prior work [30, 31], two main challenges mentioned by our participants are lack of visual information and lack of efficient ways to navigate through information.”
From Revamp: Enhancing Accessible Information Seeking Experience of Online Shopping for Blind or Low Vision Users (2021) · cites this paper as [31]
Tactile interaction remains crucial, especially for BLV people who rely on touch.
“Since 63% of consumers research products online before purchasing in-store [6], and 94% of Americans shop in physical stores [23], tactile interaction remains crucial [46, 72, 85], especially for BLV people who rely on touch [32, 88].”
From Experiences and Perceptions of Blind and Low-Vision (BLV) People with Retail Self-Checkout Systems (2026) · cites this paper as [88]
The study found that missing alternative text for UI components and images was a common accessibility problem for BVI users navigating food delivery apps with screen readers, matching other research on smartphone apps and online shopping.
“Our fndings showed that the lack of alternative text for UI components and images was a common accessibility issue for BVI people while navigating food delivery apps using screenreaders and other assistive technology devices, consistent with fndings from other studies on diferent types of smartphone applications and online shopping experiences of people with visual impairments [18,23,28].”
From Investigating the User Experience and Challenges of Food Delivery Applications for the Blind and Visually Impaired (2023) · cites this paper as [23]
Participants reported that accessibility often varies across food delivery apps because layouts, navigation patterns, and information architecture are not externally consistent.
“Similar to the fndings by Stangl et al. [2018], participants of this study also reported that accessibility often fuctuates between various food delivery applications because of the lack of external consistency in the layout, navigation patterns, and information architecture between diferent applications [23].”
From Investigating the User Experience and Challenges of Food Delivery Applications for the Blind and Visually Impaired (2023) · cites this paper as [23]
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Cites
Basics of Qualitative Research: Grounded Theory Procedures and Techniques.
Ellis, C., Strauss, A., & Corbin, J. M. (1992) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2074814 · Cited by 29,279 · see citing works
Basics of Qualitative Research: Grounded Theory Procedures and Techniques
Long, D. R., Strauss, A., & Corbin, J. M. (1993) · Modern Language Journal · doi:10.2307/328955 · Cited by 17,612 · see citing works
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works
Labeling images with a computer game
von Ahn, L., & Dabbish, L. (2004) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/985692.985733 · Cited by 2,230 · see citing works
Microsoft COCO Captions: Data Collection and Evaluation Server
Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollar, P., & Zitnick, C. L. (2015) · arXiv · doi:10.48550/arxiv.1504.00325 · Cited by 1,614 · see citing works
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
(2016) · doi:10.1109/cvpr33180.2016 · Cited by 1,569 · see citing works
Value Sensitive Design and Information Systems
Friedman, B., Kahn, P. H., & Borning, A. H. (2008) · Wiley eBooks · doi:10.1002/9780470281819.ch4 · Cited by 1,149 · see citing works
A comparison of statistical significance tests for information retrieval evaluation
Smucker, M. D., Allan, J., & Carterette, B. (2007) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1321440.1321528 · Cited by 728 · see citing works
VizWiz Grand Challenge: Answering Visual Questions from Blind People
Gurari, D., Li, Q., Stangl, A. J., Guo, A., Lin, C., Grauman, K., Luo, J., & Bigham, J. P. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00380 · Cited by 621 · cited 1× · see citing works
“, Table 2; Q4 and Q5), and more specifically responds to Lily who shared “I miss browsing through magazines to get outfit inspiration!” The importance of these chosen features is reinforced by the findings of prior work [6, 13, 14, 20, 44, 46].”
BackgroundSelf-citationVizWiz: nearly real-time answers to visual questions
Bigham, J. P., Jayant, C., Ji, H., Little, G., Miller, A., Miller, R. C., Miller, R., Tatarowicz, A., White, B., White, S., et al. (2010) · Proceedings of the 23nd annual ACM symposium on User interface software and technology · doi:10.1145/1866029.1866080 · Cited by 601 · see citing works
Self-citationBigham, J. P., Jayant, C., Ji, H., Little, G., Miller, A. E., Miller, R. C., Tatarowicz, A., White, B., White, S., & YEH, T. P. (2010) · International Cross-Disciplinary Conference on Web Accessibility (W4A) · doi:10.1145/1805986.1806020 · Cited by 536 · see citing works
Self-citationGuidelines are only half of the story: accessibility problems encountered by blind users on the web
Power, C., Freire, A., Petrie, H., & Swallow, D. (2012) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2207736 · Cited by 327 · see citing works
WhittleSearch: Image search with relative attribute feedback
Kovashka, A., Parikh, D., & Grauman, K. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248026 · Cited by 315 · see citing works
Self-citationThe relationship between accessibility and usability of websites
Petrie, H., & Kheir, O. (2007) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1240624.1240688 · Cited by 304 · see citing works
(2017) · doi:10.1145/2998181.2998364 · Cited by 258 · see citing works
Human Parsing with Contextualized Convolutional Neural Network
Liang, X., Xu, C., Shen, X., Yang, J., Liu, S., Tang, J., Lin, L., & Yan, S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.163 · Cited by 245 · see citing works
Visual challenges in the everyday lives of blind people
Brady, E. L., Morris, M. R., Zhong, Y., White, S., & Bigham, J. P. (2013) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2470654.2481291 · Cited by 236 · see citing works
Understanding Blind People's Experiences with Computer-Generated Captions of Social Media Images
MacLeod, H., Bennett, C. L., Morris, M. R., & Cutrell, E. (2017) · Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025814 · Cited by 200 · see citing works
WebInSight: making web images accessible
Bigham, J. P., Kaminsky, R. S., Ladner, R. E., Danielsson, O. M., & Hempton, G. L. (2006) · Proceedings of the 8th international ACM SIGACCESS conference on Computers and accessibility · doi:10.1145/1168987.1169018 · Cited by 161 · see citing works
Improving accessibility of the web with a computer game
von Ahn, L., Ginosar, S., Kedia, M., Liu, R., & Blum, M. (2006) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1124772.1124785 · Cited by 156 · see citing works
Jing, Y., Liu, D., Kislyuk, D., Zhai, A., Xu, J., Donahue, J., & Tavel, S. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2788621 · Cited by 143 · see citing works
Williams, M. A., Hurst, A. K., & Kane, S. K. (2013) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/2513383.2513449 · Cited by 139 · see citing works
Harrison, C., & Dey, A. K. (2008) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1357054.1357135 · Cited by 138 · see citing works
Early engagement and new technologies: Opening up the laboratory
Doorn, N., Gorman, M. E., van de Poel, I., & Schuurbiers, D. (2013) · Philosophy of engineering and technology · doi:10.1007/978-94-007-7844-3 · Cited by 136 · see citing works
People with Visual Impairment Training Personal Object Recognizers
Kacorri, H., Kitani, K., Bigham, J. P., & Asakawa, C. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025899 · Cited by 117 · see citing works
CueSee: exploring visual cues for people with low vision to facilitate a visual search task
Zhao, Y., Szpiro, S., Knighten, J., & Azenkot, S. (2016) · Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2971648.2971730 · Cited by 111 · see citing works
Takagi, H., Kawanaka, S., Kobayashi, M., Itoh, T., & Asakawa, C. (2008) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/1414471.1414507 · Cited by 106 · see citing works
Crowdsourcing subjective fashion advice using VizWiz
Burton, M. A., Brady, E. L., Brewer, R. N., Neylan, C., Bigham, J. P., & Hurst, A. K. (2012) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/2384916.2384941 · Cited by 95 · see citing works
Finding a store, searching for a product: a study of daily challenges of low vision people
Szpiro, S., Zhao, Y., & Azenkot, S. (2016) · Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2971648.2971723 · Cited by 88 · see citing works
Salisbury, E., Kamar, E. S., & Morris, M. R. (2017) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v5i1.13301 · Cited by 85 · see citing works
Toward real-time grocery detection for the visually impaired
Winlock, T., Christiansen, E. M., & Belongie, S. (2010) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2010.5543576 · Cited by 80 · see citing works
Zhai, A., Kislyuk, D., Jing, Y., Feng, M., Tzeng, E., Donahue, J., Du, Y. L., & Darrell, T. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3041021.3054201 · Cited by 75 · see citing works
The Effects of "Not Knowing What You Don't Know" on Web Accessibility for Blind Web Users
Bigham, J. P., Lin, I., & Savage, S. (2017) · Proceedings of the 19th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3132525.3132533 · Cited by 73 · see citing works
Web accessibility guidelines: A lesson from the evolving Web
Harper, S., & Chen, A. Q. (2012) · World Wide Web · doi:10.1007/s11280-011-0130-8 · Cited by 72 · see citing works
Robot-assisted shopping for the blind: issues in spatial cognition and product selection
Gharpure, C. P., & Kulyukin, V. (2008) · Intelligent Service Robotics · doi:10.1007/s11370-008-0020-9 · Cited by 71 · see citing works
Zhong, Y., Lasecki, W. S., Brady, E. L., & Bigham, J. P. (2015) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702437 · Cited by 68 · see citing works
Remote Assistance for Blind Users in Daily Life: A Survey about Be My Eyes
Avila, M., Wolf, K., Brock, A., & Henze, N. (2016) · Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments · doi:10.1145/2910674.2935839 · Cited by 59 · see citing works
Improving GUI accessibility for people with low vision
Kline, R. L., & Glinert, E. P. (1995) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/223904.223919 · Cited by 53 · see citing works
A framework of assistive pointers for low vision users
Fraser, J., & Gutwin, C. A. (2000) · doi:10.1145/354324.354329 · Cited by 51 · see citing works
Stearns, L., DeSouza, V., Yin, J., Findlater, L., & Froehlich, J. E. (2017) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3132525.3134812 · Cited by 50 · see citing works
Making the web easier to see with opportunistic accessibility improvement
Bigham, J. P. (2014) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2642918.2647357 · Cited by 36 · see citing works
Clothing matching for visually impaired persons
Yuan, S., Tian, Y., & Arditi, A. R. (2011) · Technology and Disability · doi:10.3233/tad-2011-0313 · Cited by 33 · see citing works
Web Page Segmentation: A Review
Web Ergonomics Laboratory, #., & Yeşilada, Y. (2014) · Figshare · doi:10.6084/m9.figshare.979322 · Cited by 25 · see citing works
Web Page Segmentation: A Review
Laboratory, #. W. E., & Yesilada, Y. (2014) · Figshare · doi:10.6084/m9.figshare.979322.v1 · Cited by 23 · see citing works
Jas, M., & Parikh, D. (2015) · Cited by 17 · see citing works
Visual Discovery at PinterestPreprint
Zhai, A., Kislyuk, D., Jing, Y., Feng, M., Tzeng, E., Donahue, J., Du, Y. L., & Darrell, T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.04680 · Cited by 14 · see citing works
Lasecki, W. S., Kushalnagar, R., & Bigham, J. P. (2014) · doi:10.1145/2661334.2661352 · Cited by 13 · see citing works
Dinesh, T. B., Üsküdarlı, S., Sastry, S., Aggarwal, D., & Choppella, V. (2012) · International Cross-Disciplinary Conference on Web Accessibility (W4A) · doi:10.1145/2207016.2207030 · Cited by 10 · see citing works
Choosing which clothes to wear confidently: A tool for pattern matching
Paisios, N., Rubinsteyn, A., & Subramanian, L. (2012) · Cited by 9 · see citing works
Electronic Shopping Cart Facility for Blind People Using USB Firmware
Jethava, H., Zafar, S., & Saini, M. (2014) · Cited by 7 · see citing works
An Aspect Oriented Approach for Renarrating Web Content
Prasad, G. V. S., Chimalakonda, S., Choppella, V., & Reddy, Y. R. (2017) · Innovations in Software Engineering Conference (ISEC) · doi:10.1145/3021460.3021466 · Cited by 7 · see citing works
Alali, R. M. (2017) · Cardinal Scholar (Ball State University) · Cited by 5 · see citing works
Human Curation and Convnets: Powering Item-to-Item Recommendations on PinterestPreprint
Kislyuk, D., Liu, Y., Liu, D., Tzeng, E., & Jing, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.04003 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- [14]14. T. B. Dinesh, S Uskudarli, S. Sastry, D. Aggarwal, and V. Choppella. 2012. Alipi: A framework for re-narrating web pages. In Proceedings of the International Cross-Disciplinary Conference on Web Accessibility.
- [19]19. S. Harper and A. Q. Chen. 2012. Web accessibility guidelines. Web accessibility guidelines 15, 1 (2012), 61-88.
- [27]27. S. Kesh, S. V. Venkateshwar, and A. U. 2017. Shopping By Blind People: Detection of Interactions in Ambient Assisted Living Environments using RFID. In International Journal of Wireless Communications and Networking Technologies, Vol. 6. 7-11.
- [51]51. Y. Zhong, W. S. Lasecki, E. Brady, and J. P. Bigham. 2015. Regionspeak: Quick comprehensive spatial descriptions of complex images for blind users. In ACM Conference on Human Factors in Computing Systems (CHI). 2353-2362. Session 3: Accessing Information ASSETS'18, October 22-24, 2018, Galway, Ireland
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Sorted by citations
Stangl, A., Morris, M. R., & Gurari, D. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376404 · Cited by 139 · see citing works
“Automated image description approaches employ artificial intelligence models to generate image descriptions [24, 14, 38, 37, 43, 35].”
BackgroundMethodInfluentialSelf-citationBennett, C. L., Gleason, C., Scheuerman, M. K., Bigham, J. P., Guo, A., & To, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445498 · Cited by 128 · see citing works
“As with prior work [73, 104, 105], screen reader users have contextualized preferences for what is considered important in image descriptions.”
BackgroundStangl, A., Verma, N., Fleischmann, K. R., Morris, M. R., & Gurari, D. (2021) · Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3441852.3471233 · Cited by 81 · see citing works
“Others are fully-automated approaches that employ computer vision algorithms to describe, for example, the objects, people, and scenery in images [21, 22, 30, 38, 41, 71, 73, 75, 82].”
BackgroundMethodInfluentialSelf-citationSocial Sensemaking with AI: Designing an Open-ended AI Experience with a Blind Child
Morrison, C. P., Cutrell, E. B., Grayson, M., Thieme, A., Taylor, A. S., Roumen, G. J., Longden, C., Tschiatschek, S., Marques, R. F., & Sellen, A. J. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445290 · Cited by 48 · see citing works
“Focusing the design process of open-ended AI systems around a lack of capability [35], the need for a superpower [33], or inaccessible tasks [52] will not address this initial capability uncertainty.”
BackgroundA systematic review of intelligent assistants
Islas-Cota, E., Gutiérrez-García, J. O., Acosta, C. O., & Rodríguez, L.-F. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.09.035 · Cited by 46 · see citing works
Making Short-Form Videos Accessible with Hierarchical Video Summaries
Daele, T. V., Iyer, A., Zhang, Y., Derry, J. C., Huh, M., & Pavel, A. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642839 · Cited by 40 · see citing works
“For instance, prior work revealed that BLV people want adaptive length descriptions for digital comics [32], shopping images [67], and urban scenes [31].”
Wang, R., Chen, Z., Zhang, M. R., Li, Z., Liu, Z., Dang, Z., Yu, C., & Chen, X. '. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445547 · Cited by 42 · see citing works
“formation signifcantly reduce BLV consumers’ engagement with online shopping websites [30, 31].”
BackgroundInfluentialDas, M., Fiannaca, A., Morris, M. R., Kane, S., & Bennett, C. L. (2024) · International Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642325 · Cited by 41 · see citing works
Everyday Uncertainty: How Blind People Use GenAI Tools for Information Access
Tang, X., Abdolrahmani, A., Gergle, D., & Piper, A. M. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713433 · Cited by 30 · see citing works
Mapping 20 years of accessibility research in HCI: A co-word analysis
Sarsenbayeva, Z., van Berkel, N., Hettiachchi, D., Tag, B., Velloso, E., Gonçalves, J., & Kostakos, V. (2023) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2023.103018 · Cited by 33 · see citing works
Context-Aware Image Descriptions for Web Accessibility
Mohanbabu, A. G., & Pavel, A. (2024) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3663548.3675658 · Cited by 29 · see citing works
“…explored alternative formats to enable gaining more information on demand by accessing a location in the image [40, 50, 52], gaining information through progressive detail or information type [37, 50], selecting a description modality [27, 50], or by asking questions about the image [1, 50, 61].”
Inclusive Making: designing tools and experiences to promote accessibility and redefine making
Worsley, M. B., & Bar-El, D. (2020) · Computer Science Education · doi:10.1080/08993408.2020.1863705 · Cited by 29 · see citing works
“…where the cost of the technology makes it difficult for users to purchase or utilize (e.g., cost to purchase or repair assistive technology – (Stangl et al., 2018)) or where monetary gains or losses are a driving factor (e.g., corporate interest in Makerspaces (Blikstein & Worsley, 2016;…”
BackgroundImage Accessibility for Screen Reader Users: A Systematic Review and a Road Map
Oh, U., Joh, H., & Lee, Y. (2021) · Electronics · doi:10.3390/electronics10080953 · Cited by 29 · see citing works
Barbosa, N. M., Hayes, J., Kaushik, S., & Wang, Y. (2022) · ACM Transactions on Accessible Computing · doi:10.1145/3519032 · Cited by 28 · see citing works
Simons, R. N., Gurari, D., & Fleischmann, K. R. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415176 · Cited by 27 · see citing works
Self-citationJung, J. Y., Steinberger, T., Kim, J., & Ackerman, M. S. (2022) · Designing Interactive Systems Conference · doi:10.1145/3532106.3533522 · Cited by 23 · see citing works
What's in an ALT Tag? Exploring Caption Content Priorities through Collaborative Captioning
Muehlbradt, A., & Kane, S. K. (2022) · ACM Transactions on Accessible Computing · doi:10.1145/3507659 · Cited by 22 · see citing works
“Researchers have explored issues related to creating image descriptions for fashion [3, 15, 46], social media content [23, 50, 54, 55], and artistic images [12].”
BackgroundCocomix: Utilizing Comments to Improve Non-Visual Webtoon Accessibility
Huh, M., Lee, Y., Choi, D., Kim, H., Oh, U., & Kim, J. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502081 · Cited by 22 · see citing works
Choi, D., Lee, U., & Hong, H. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517574 · Cited by 21 · see citing works
“For example, researchers attempted to help people with vision impairments identify objects through an image recognition algorithm [53], and developed an AI system that could assist with their online shopping activities [84].”
BackgroundVision Skills Needed to Answer Visual Questions
Zeng, X., Wang, Y., Chiu, T.-Y., Bhattacharya, N., & Gurari, D. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415220 · Cited by 21 · see citing works
“More generally, our work fits into the broad collection of work that seeks to better understand and meet the needs of people with visual impairments [9, 18, 20, 29, 30, 32, 49–51].”
BackgroundSelf-citationSupporting Novices Author Audio Descriptions via Automatic Feedback
Natalie, R., Tseng, J., Kacorri, H., & Hara, K. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581023 · Cited by 19 · see citing works
“Much work has focused on adding accessibility to still images by computationally generating image descriptions [9, 21, 25, 26, 50, 51].”
BackgroundVizWiz-FewShot: Locating Objects in Images Taken by People with Visual Impairments
Tseng, Y., Bell, A., & Gurari, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20074-8_33 · Cited by 17 · see citing works
Self-citationAccessibility-Related Publication Distribution in HCI Based on a Meta-Analysis
Colley, M., Kränzle, T., & Rukzio, E. (2022) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3491101.3519701 · Cited by 15 · see citing works
Cut Quality Estimation in Industrial Laser Cutting Machines: A Machine Learning Approach
Santolini, G., Rota, P., Gandolfi, D., & Bosetti, P. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00052 · Cited by 14 · see citing works
“Nowadays Machine learning finds wide application on a variety of fields such as healthcare applications [18, 21], security and tracking [25, 6], sport analysis [7, 4], societal challenges [9, 20].”
BackgroundLee, Y.-J., Joh, H., Yoo, S., & Oh, U. (2022) · ACM Transactions on Accessible Computing · doi:10.1145/3555720 · Cited by 13 · see citing works
Supporting Object-level Exploration of Artworks by Touch for People with Visual Impairments
Kwon, N., Koh, Y., & Oh, U. (2019) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3308561.3354620 · Cited by 13 · see citing works
“This process will be crowdsourced like LabelMe [16] or fully automated as in [13, 17] in the next version.”
BackgroundMethodAlluqmani, A., Harvey, M. A., & Zhang, Z. (2023) · Proceedings of the 2023 ACM Designing Interactive Systems Conference · doi:10.1145/3563657.3595978 · Cited by 14 · see citing works
“According to these results, together with the findings of previous work [25, 27], it seems that VI people would welcome new assistive technology.”
BackgroundResultInfluentialLee, Y.-J., Joh, H., Yoo, S., & Oh, U. (2021) · International Web for All Conference (W4A) · doi:10.1145/3430263.3452425 · Cited by 12 · see citing works
“BrowseWithMe [45] also applied computer vision techniques to implement a system that automatically describes photos of clothes on online shopping malls for PVI.”
BackgroundMethodSalient Object Detection for Images Taken by People With Vision Impairments
Reynolds, J., Nagesh, C. K., & Gurari, D. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00833 · Cited by 11 · see citing works
“Such models would also support more efficient learning about the salient content in images by restricting image descriptions to focus only on that content [4] or rapidly magnifying that content for low vision users’ inspection [20, 44].”
BackgroundSelf-citationKamikubo, R., Kacorri, H., & Asakawa, C. (2024) · International Web for All Conference (W4A) · doi:10.1145/3677846.3677860 · Cited by 9 · see citing works
“…guidance to supplement visual information of consumer staples [12, 83, 119, 128, 129] and applications that indicate clothing patterns and colors [105–107 Despite the large body of work on accessible shopping for blind people, supporting recreational window shopping remains unexplored, with a…”
BackgroundRyskeldiev, B., Hara, K., Kobayashi, M., & Kusano, K. (2022) · Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3517428.3550390 · Cited by 8 · see citing works
Kwon, N., Lee, Y.-J., & Oh, U. (2021) · Universal Access in the Information Society · doi:10.1007/s10209-021-00814-2 · Cited by 7 · see citing works
TableNarrator: Making Image Tables Accessible to Blind and Low Vision People
Mo, Y., Huang, G., Li, L., Deng, D., Yu, Z., Xu, Y., Ye, K., Zhou, S., & Bu, J. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714329 · Cited by 6 · see citing works
Semantic table-of-contents for efficient web screen reading
Ferdous, M. J., Uddin, S., & Ashok, V. G. (2021) · ACM Symposium on Applied Computing · doi:10.1145/3412841.3442066 · Cited by 6 · see citing works
“Researchers have also proposed accessibility assistants for assisting blind users to efficiently interact with webpages using either natural language or special input gestures [8, 12, 24, 34, 39].”
BackgroundSreedhar, R., Tan, N., Zhang, J., Jin, K., Gregson, S., Moreta-Feliz, E., Samudrala, N., & Sadalgi, S. (2022) · International Web for All Conference (W4A) · doi:10.1145/3493612.3520453 · Cited by 6 · see citing works
Accessibility of Profile Pictures: Alt Text and Beyond to Express Identity Online
Mott, M. E., Tang, J. C., & Cutrell, E. B. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580710 · Cited by 6 · see citing works
“Style of dress has been proposed as a detail of interest for images that appear on e-commerce sites [26] but our fndings suggest that similar details might be benefcial on platforms like LinkedIn that are tailored toward hiring and job seeking.”
BackgroundYu, Y., Ryskeldiev, B., Tsutsui, A., Gillingham, M., & Wang, Y. (2025) · Proceedings of the 27th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3663547.3746353 · Cited by 8 · see citing works
“This selection was informed by user interviews in the formative study, which provided insights into platform familiarity and usage patterns.”
InfluentialNair, A., Mathew, R., Mei, Z., Milallos, R., Chelladurai, P. K., & Oh, T. (2023) · Proceedings of the 25th International Conference on Mobile Human-Computer Interaction · doi:10.1145/3565066.3608688 · Cited by 5 · see citing works
“Although online services such as food delivery platforms ofer a better alternative to BVI people than navigating to physical stores and locations [25], these accessibility issues erode the likely benefts they derive from these apps.”
BackgroundGupta, K., Takaki, K., Tsutsui, A., Teramoto, K., & Ryskeldiev, B. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3706665 · Cited by 5 · see citing works
“We used semi-structured interviews and user journey mapping, drawing from established HCI methodologies in previous accessibility and e-commerce studies [18, 20].”
Thadikaran, G. B., & Singh, S. K. (2024) · Organizations and Markets in Emerging Economies · doi:10.15388/omee.2024.15.5 · Cited by 5 · see citing works
Ryskeldiev, B., Kobayashi, M., Teramoto, K., & Kusano, K. (2023) · Asian HCI Symposium · doi:10.1145/3604571.3604586 · Cited by 4 · see citing works
“[9] which featured an online smart assistant which would process web store content and present it in an accessible manner through a conversational smart assistant.”
BackgroundA Prompt Chaining Framework for Long-Term Recall in LLM-Powered Intelligent Assistant
Seo, S., Yoo, S., & Jang, Y. (2025) · Proceedings of the 30th International Conference on Intelligent User Interfaces · doi:10.1145/3708359.3712117 · Cited by 3 · see citing works
Kim, S., Lee, S., Kim, K., & Oh, U. (2024) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3640543.3645154 · Cited by 3 · see citing works
“There has been active research in automating image captioning using computer vision to improve image accessibility [20, 22, 30, 32, 35].”
BackgroundNetizen A11y: Engaging Internet Users in Making Visual Media Accessible
Wang, R. (2024) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3640544.3645247 · Cited by 2 · see citing works
“However, prior studies [13, 15] show that BLV people still face significant barriers in efficiently seeking required information from online shopping websites due to inadequate image descriptions and screen readers’ inability to effectively filter out a large amount of information on a product page.”
BackgroundNicoson, E., & Ha‐Brookshire, J. (2025) · Societies · doi:10.3390/soc15040090 · Cited by 2 · see citing works
Medina, M. J., Lalanne, D., & Baudet, C. (2022) · doi:10.1145/3502178.3529111 · Cited by 2 · see citing works
Tactile and sensory fashion garments for the visually impaired-challenges and opportunities
Alsabhi, R. (2025) · International Journal of Fashion Design Technology and Education · doi:10.1080/17543266.2024.2448243 · Cited by 1 · see citing works
“…do not have access to visual information, it may be difficult for them to choose appropriate clothing for a variety of occasions or to understand the visual components of fashion, such as how colours should be coordinated or which accessories should be worn together (Stangl et al., 2018).”
CoSight: Exploring Viewer Contributions to Online Video Accessibility Through Descriptive Commenting
Wang, R., Liu, X., Wang, B., Zhang, W., Liao, Z., Li, Z., Pavel, A., & Chen, X. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747747 · Cited by 1 · see citing works
“Our work is informed by prior research on authoring tools for writing descriptions, utilizing human-authored texts to enhance accessibility, and fostering contributions for accessibility and social good.”
Pal, R., Kar, S., & Sekh, A. A. (2024) · SN Computer Science · doi:10.1007/s42979-024-03351-w · Cited by 1 · see citing works
“Stangl et al. [46] explored these challenges and implemented computer vision algorithms to develop intelligent online shopping assistants.”
Visually Impaired Assistance with Large Models
Xiang, R., Zhao, Y. H., Zhang, Y., Li, J., Liao, M., & Li, Y. (2024) · IEEE Smart World Congress (SWC) · doi:10.1109/swc62898.2024.00253 · Cited by 1 · see citing works
Park, S., Jung, H., Ryskeldiev, B., Lee, J., Lee, G., Jeong, H., Chun, M., & Teramoto, K. (2024) · The 26th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3663548.3688522
“Stangl et al. [20] proposed chatbots that only allow voice input from users, with the aim of assisting visually impaired users in accessing product information by creating an assistant that receives voice queries from users and retrieves product information from the relevant web pages.”
AR Haptic-Audio Conversion for Non-Visual Product Understanding in Smartphone AR
Tokida, S., Tsutsui, A., Kobori, N., Gillingham, M., & Ryskeldiev, B. (2026) · Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772363.3798874
Experiences and Perceptions of Blind and Low-Vision (BLV) People with Retail Self-Checkout Systems
Heisey, A., Shinohara, K., & Tigwell, G. W. (2026) · ACM Transactions on Accessible Computing · doi:10.1145/3816243
Haptic Auditory Feedback for Enhanced Image Description: A Study of User Preferences and Performance
Alkhathlan, M., Tlachac, M., & Rundensteiner, E. A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42280-5_14 · Cited by 1 · see citing works
An Automated Tool for Creating Clothing Catalog Databases: MyEyes–Fashion
Carvalho, M., Rocha, D., & Carvalho, V. H. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-67307-8_5
Bridging the Accessibility Gap in Online Shopping with AI - Driven Solutions
Wroblewski, C., Mirza, A. B., & Lin, W. (2024) · International Conference on Computer and Applications (ICCA) · doi:10.1109/icca62237.2024.10927778
The Use of Artificial Intelligence in Building Sign Language Recognition Application
Tahiri, D., & Topi, A. (2024) · Ingenious · doi:10.58944/uucp6635
iSight: A Smart Clothing Management System to Empower Blind and Visually Impaired Individuals
Rocha, D., Leão, C. P., Soares, F., & Carvalho, V. H. (2025) · Information · doi:10.3390/info16050383
Automatic Accessible Multimodal Translation of Graphics Using A Refreshable Pin Array
Kim, S.-G., Kim, A., Kim, E., Chung, M., & Yoo, Y. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3754586
“BLVIs are also interested in fashion [41], but the current online shopping malls provide Alt-Text or accessible features to a limited extent, despite the pages being image-centric.”
Emerging Trends in Artificial Intelligence and Blindness: A Delphi Study
Vader, M.-L., Baguhn, S. J., & Silverman, A. M. (2025) · Journal of Visual Impairment & Blindness · doi:10.1177/0145482x251399194
“The best way to design assistive technology so it meets the needs and wants of individuals with disabilities is to include individuals from the target user population in the entire design process (Morrison et al., 2017; Stangl et al., 2018).”
Blankson-Stiles-Ocran, S., Tengan, C., Sakyi, R., & Awusiedu, R. O. A. (2026) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-80686-169-920261005
Ryskeldiev, B., Gillingham, M., & Kobori, N. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3799042
Toward Independent Online Shopping of the Visually Impaired Through Voice-based Computer-Using Agent
Shin, S., Oh, J., Kim, S., Eom, S., & Lee, S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791681
Reclaiming the Future: Visions of AI and Disability in the 2020s
Vader, M.-L., Romero, E. M., Heydarian, C. H., Baguhn, S. J., & Silverman, A. M. (2026) · International Web for All Conference (W4A) · doi:10.1145/3800424.3800611
Generating clothing graphic captions for visually impaired users
Alluqmani, A., Harvey, M., & Paramita, M. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21774-w
Song, H., Shin, M., Choi, J., Kim, Y., Jang, K., Suh, B., & Jung, H. (2026) · Proceedings of the 2026 28th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3797867.3828985
- Authors
- Stangl, A. J., Kothari, E., Jain, S. D., Yeh, T., Grauman, K., & Gurari, D.
- Affiliations
- University of Colorado Boulder (US)
- The University of Texas at Austin (US)
- Meta (United States) (US)
- Venue
- Proceedings of the 20th International ACM SIGACCESS Conference on Computers and Accessibility
- Publisher
- ACM
- Published
- 2018-10-08
- Pages
- 107-118
- OpenAlex
- W2896108493
- Cited by
- 66
- References
- 50
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 14 · 2023: 7 · 2022: 11 · 2021: 9 · 2020: 4 · 2019: 2
- FWCI
- 6.46
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) (publishedVersion)
- Funders
- National Science Foundation (IIS-1755593)
- Amazon Research Award
- Topics
- Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
- Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- visual impairment, web accessibility, assistive technology, image description generation, natural language question answering
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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