Exploring an Ambiguous Technique for Eyes-Free Mobile Text Entry

Gaines, Dylan

doi:10.1145/3234695.3240991

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…It has opened a new era for accessible usage of the smartphone for visually impaired people [40,99]. To assist visually impaired people in filling in the input content, researchers proposed some input solutions rely on indicating Braille mappings to enter characters [14,18,24,47,48,67,88,90,102,123]. While input methods to assist mobile text entry have been extensively studied in the recent literature [79,81], text entry research has focused much less on the needs of persons with vision problems.…”

Section: Gui Understanding and Intelligentmentioning

confidence: 99%

Unblind Text Inputs: Predicting Hint-text of Text Input in Mobile Apps via LLM

Liu,

Chen,

Wang

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Mobile apps have become indispensable for accessing and participating in various environments, especially for low-vision users. Users with visual impairments can use screen readers to read the content of each screen and understand the content that needs to be operated. Screen readers need to read the hint-text attribute in the text input component to remind visually impaired users what to fill in. Unfortunately, based on our analysis of 4,501 Android apps with text inputs, over 76% of them are missing hint-text. These issues are mostly caused by developers' lack of awareness when considering visually impaired individuals. To overcome these challenges, we developed an LLM-based hint-text generation model called HintDroid, which analyzes the GUI information of input components and uses in-context learning to generate the hint-text. To ensure the quality of hint-text generation, we further designed a feedback-based inspection mechanism to further adjust hint-text. The automated experiments demonstrate the high BLEU and a user study further confirms its usefulness. HintDroid can not only help visually impaired individuals, but also help ordinary people understand the requirements of input components. HintDroid demo video: https://youtu.be/FWgfcctRbfI.

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Section: Gui Understanding and Intelligentmentioning

confidence: 99%

Unblind Text Inputs: Predicting Hint-text of Text Input in Mobile Apps via LLM

Liu,

Chen,

Wang

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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show abstract

“…Despite technological advancements, text input for the visually impaired people remains a hurdle. Although there has been a widespread adoption of smartphones and screen-readers such as Talkback [11] and VoiceOver [12] by visually impaired users, typing on mobile phones remains slow and laborious for them [1,2,3,7]. This is even more so for Indian languages.…”

Section: Introductionmentioning

confidence: 99%

“…Researchers have explored the effect of adding speech recognition technologies to keyboards. Traditionally, researchers have reported text entry speeds ranging from 19 to 53 words per minute (WPM) (or 95 to 256 CPM) for English [2,16,17,21]. After adding speech, Ruan et al reported 161 WPM (about 805 CPM) for English, 108 WPM (about 540 CPM) for Mandarin Chinese as compared to 53 WPM (about 265 CPM) and 38 WPM (about 195 CPM) without speech, respectively [16,17].…”

Section: Introductionmentioning

confidence: 99%

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Faster and Less Error-Prone: Supplementing an Accessible Keyboard with Speech Input

Bhikne

Joshi

et al. 2019

Human-Computer Interaction – INTERACT 2019

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Swarachakra is an Abugida text input keyboard available in 12 Indian languages. We enhanced an accessible version of Swarachakra Marathi with speech input. However, speech input could be error-prone, and especially so for languages where speech recognition technologies are new. Such errors could either slow the user down due to the need for editing, or go unnoticed, leading to high uncorrected error rates. We therefore conducted a withinsubject empirical study to compare the user performance of keyboard-only input method with keyboard+speech input method with 11 novice visually impaired users. We found that keyboard+speech input was almost 11 times faster, reaching 182 characters per minute, and had a lower uncorrected error rate than the keyboard-only input, and in spite of having higher corrected error rates. Though we used a wide variety of phrases in our study, we observed that all phrases were faster on average with the keyboard+speech input method. To the best of our knowledge, ours is the first empirical study to evaluate the performance of speech enabled text input in Marathi for visually impaired people. This is the highest reported speed by visually impaired users in any Indian language.

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A Wearable Input Mechanism for Blind Users of Computers Based on Mental Mapping of Thumb-To-Phalanx Distances

Naik

2019

The 21st International ACM SIGACCESS Conference on Computers and Accessibility

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Exploring an Ambiguous Technique for Eyes-Free Mobile Text Entry

Cited by 6 publications

References 5 publications

Unblind Text Inputs: Predicting Hint-text of Text Input in Mobile Apps via LLM

Unblind Text Inputs: Predicting Hint-text of Text Input in Mobile Apps via LLM

Faster and Less Error-Prone: Supplementing an Accessible Keyboard with Speech Input

A Wearable Input Mechanism for Blind Users of Computers Based on Mental Mapping of Thumb-To-Phalanx Distances

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