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Automated Testing of Refreshable Braille Display

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Human-Centric Computing in a Data-Driven Society (HCC 2020)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 590))

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Abstract

A majority of visually impaired population of India and other developing economies live in poverty. Accessibility without affordability has little meaning to this population. Assistive technology has great potential to make education accessible to this population, e.g. through refreshable Braille display devices. However, most existing solutions in this space remain out of reach for these users due to high cost. Innovation in data science and software engineering can play an important role in making assistive technological solutions affordable and accessible. In this paper, we present a machine-learning based automated testing approach that has played an important role in enabling us to design one of the most affordable refreshable Braille display devices of the world. The key component of our approach is a visual inspection module (VIM) created using Convolutional Neural Networks (CNNs). In our experiment, our model was able to detect malfunction of a Refreshable Braille display with 97.3% accuracy. Our model is small enough to be run on a battery-powered computer in real- time. Such accurate automatic testing methods have the potential to significantly reduce the cost of RBDs.

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Acknowledgement

This work was supported by Karnataka Innovation Technology Society, Dept. of IT, BT and ST, Govt. of Karnataka

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Authors and Affiliations

  1. IIIT-B, Bangalore, India

    Shivam Kumar Singh, Sujit Kumar Chakrabarti & Dinesh Babu Jayagopi

Authors
  1. Shivam Kumar Singh
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  2. Sujit Kumar Chakrabarti
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  3. Dinesh Babu Jayagopi
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Corresponding author

Correspondence to Shivam Kumar Singh .

Editor information

Editors and Affiliations

  1. University of Salford, Salford, UK

    David Kreps

  2. Chuo University, Tokyo, Japan

    Taro Komukai

  3. Anna University, Chennai, India

    T. V. Gopal

  4. Chuo University, Tokyo, Japan

    Kaori Ishii

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Singh, S.K., Chakrabarti, S.K., Jayagopi, D.B. (2020). Automated Testing of Refreshable Braille Display. In: Kreps, D., Komukai, T., Gopal, T.V., Ishii, K. (eds) Human-Centric Computing in a Data-Driven Society. HCC 2020. IFIP Advances in Information and Communication Technology, vol 590. Springer, Cham. https://doi.org/10.1007/978-3-030-62803-1_15

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  • DOI: https://doi.org/10.1007/978-3-030-62803-1_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62802-4

  • Online ISBN: 978-3-030-62803-1

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