BrailleÉcran: A Braille Approach to Text Entry on Smartphones

Siqueira, Joyce; Soares, Fabrízzio; Silva, Cleyton Rafael Gomes; Berretta, Luciana de Oliveira; Ferreira, Cristiane B. R.; Felix, Igor Moreira; Luna, Mateus Machado

doi:10.1109/compsac.2016.5

Cited by 16 publications

(9 citation statements)

References 4 publications

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“…Accessibility is a major challenge faced by visually impaired people when using these software applications. Although many researchers have focused on making touch screens viable for the visually impaired, it is still a cumbersome process [25][26][27].…”

Section: Braille Input Mechanismmentioning

confidence: 99%

Deep learning scheme for character prediction with position-free touch screen-based Braille input method

Shokat

Riaz

Rizvi

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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Smart devices are effective in helping people with impairments, overcome their disabilities, and improve their living standards. Braille is a popular method used for communication by visually impaired people. Touch screen smart devices can be used to take Braille input and instantaneously convert it into a natural language. Most of these schemes require location-specific input that is difficult for visually impaired users. In this study, a position-free accessible touchscreen-based Braille input algorithm is designed and implemented for visually impaired people. It aims to place the least burden on the user, who is only required to tap those dots that are needed for a specific character. The user has input English Braille Grade 1 data (a–z) using a newly designed application. A total dataset comprised of 1258 images was collected. The classification was performed using deep learning techniques, out of which 70%–30% was used for training and validation purposes. The proposed method was thoroughly evaluated on a dataset collected from visually impaired people using Deep Learning (DL) techniques. The results obtained from deep learning techniques are compared with classical machine learning techniques like Naïve Bayes (NB), Decision Trees (DT), SVM, and KNN. We divided the multi-class into two categories, i.e., Category-A (a–m) and Category-B (n–z). The performance was evaluated using Sensitivity, Specificity, Positive Predicted Value (PPV), Negative Predicted Value (NPV), False Positive Rate (FPV), Total Accuracy (TA), and Area under the Curve (AUC). GoogLeNet Model, followed by the Sequential model, SVM, DT, KNN, and NB achieved the highest performance. The results prove that the proposed Braille input method for touch screen devices is more effective and that the deep learning method can predict the user's input with high accuracy.

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Section: Braille Input Mechanismmentioning

confidence: 99%

Deep learning scheme for character prediction with position-free touch screen-based Braille input method

Shokat

Riaz

Rizvi

et al. 2020

Hum. Cent. Comput. Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…This is unperceptive for blind users, which lead to some confusion on whether the bottom or top part of the screen were actually being pressed. Some participants suggested the use of markers on this areas to overcome this issue, which could be done with a 3D printed surface, as in [27]. It is, still a hardware-specific issue, as other watches have thinner or zero border.…”

Section: Discussionmentioning

confidence: 99%

“…One finger selects the left or right dot, two fingers select both and three fingers don't select any dots. BrailleÉcran [27] has a tactile 3D printed film, which overlays the smartphone for braille text entry. SingleTapBraille [2] eliminates the need for finding specific locations on screen, allowing information to be entered, using a single finger.…”

Section: Braille Text Entry On Touchscreen Devicesmentioning

confidence: 99%

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Braille text entry on smartwatches

Luna

Soares

Nascimento

et al. 2019

Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems

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Smartwatches are gaining popularity on market with a set of features comparable to smartphones in a wearable device. This novice technology brings new interaction paradigms and challenges for blind users, who have difficulties dealing with touchscreens. Among a variety of tasks that must be studied, text entry is analyzed, considering that current existing solutions may be unsatisfactory (as voice input) or even unfeasible (as working with tiny QWERTY keyboards) for a blind user. More specifically, this paper presents a study on possible solutions for composing a Braille cell on smartwatches. Five prototypes were developed and different feedback features were proposed. These are confronted with seven specialists on an evaluation study that results in a qualitative analysis of which strategies can be more useful for blind users in a Braille text entry.

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“…A tactile interface cover for touchscreen phones was designed by [40]. is cover, called Braille Ecran, which provides an interface that consists of six Braille Dots.…”

Section: Braille Ecranmentioning

confidence: 99%

Analysis and Evaluation of Braille to Text Conversion Methods

Shokat

Riaz

Rizvi

et al. 2020

Mobile Information Systems

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Technology is advancing rapidly in present times. To serve as a useful and connected part of the community, everyone is required to learn and update themselves on innovations. Visually impaired people fall behind in this regard because of their inherent limitations. To involve these people as active participants within communities, technology must be modified for their facilitation. This paper provides a comprehensive survey of various user input schemes designed for the visually impaired for Braille to natural language conversion. These techniques are analyzed in detail with a focus on their accessibility and usability. Currently, considerable effort has been made to design a touch-screen input mechanism for visually impaired people, such as Braille Touch, Braille Enter, and Edge Braille. All of these schemes use location-specific input and challenge visually impaired persons to locate specified places on the touch screen. Most of the schemes require special actions to switch between upper and lowercase and between numbers and special characters, which affects system usability. The key features used for accessing the performance of these techniques are efficiency, accuracy, and usability issues found in the applications. In the end, a comparison of all these techniques is performed. Outcomes of this analysis show that there is a strong need for application that put the least burden on the visually impaired users. Based on this survey, a guideline has been designed for future research in this area.

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BrailleÉcran: A Braille Approach to Text Entry on Smartphones

Cited by 16 publications

References 4 publications

Deep learning scheme for character prediction with position-free touch screen-based Braille input method

Deep learning scheme for character prediction with position-free touch screen-based Braille input method

Braille text entry on smartwatches

Analysis and Evaluation of Braille to Text Conversion Methods

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