Skip to main content

Artificial Intelligence-Based L&E-Refiner for Blind Learners

  • Conference paper
  • First Online:
ICT: Innovation and Computing (ICTCS 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 879))

  • 37 Accesses

Abstract

An Artificial Intelligence (AI)-based scribe known as L &E Refiner for blind learners is a technology that utilizes natural language processing and machine learning techniques to automatically transcribe lectures, books, and other written materials into audio format. This system is designed to provide an accessible learning experience for blind students, allowing them to easily access and interact with educational content. The AI scribe is able to recognize and understand various forms of text, including handwriting, printed text, and digital documents, and convert them into speech output that blind learners easily comprehend. This technology has the potential to significantly improve the accessibility and inclusion of education for blind individuals.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ali SA (2023) Artificial intelligence techniques to understand braille: a language for visually impaired individuals. In: Handbook of research on artificial intelligence applications in literary works and social media. IGI Global, pp 254–276

    Google Scholar 

  2. Bharadwaj KA, Joshi MM, Kumbale NS, Shastry NS, Panimozhi K, Choudhury AR (2020) Speech automated examination for visually impaired students. In: 2020 2nd international conference on innovative mechanisms for industry applications (ICIMIA). IEEE, pp 378–381

    Google Scholar 

  3. Dhou S, Alnabulsi A, Al-Ali A, Arshi M, Darwish F, Almaazmi S, Alameeri R (2022) An IoT machine learning-based mobile sensors unit for visually impaired people. Sensors 22(14):5202

    Article  Google Scholar 

  4. Fernandes DL, Ribeiro MHF, Cerqueira FR, Silva MM (2022) Describing image focused in cognitive and visual details for visually impaired people: an approach to generating inclusive paragraphs. arXiv preprint arXiv:2202.05331

  5. Ghatwary N, Abouzeina A, Kantoush A, Eltawil B, Ramadan M, Yasser M (2022) Intelligent assistance system for visually impaired/blind people (ISVB). In: 2022 5th international conference on communications, signal processing, and their applications (ICCSPA). IEEE, pp 1–7

    Google Scholar 

  6. Jadhav A, Padwad H, Chandak M, Raut R (2022) Use of assistive techniques for the visually impaired people. Intelligent systems for rehabilitation engineering, pp 115–127

    Google Scholar 

  7. Mina PNR, Solon IM, Sanchez FR, Delante TK, Villegas JK, Basay FJ, Andales Jr, Pasko F, Estrera MFR, Samson Jr, R et al (2023) Leveraging education through artificial intelligence virtual assistance: a case study of visually impaired learners. Int J Educ Innov Res 2(1):10–22

    Google Scholar 

  8. Mukhiddinov M, Djuraev O, Akhmedov F, Mukhamadiyev A, Cho J (2023) Masked face emotion recognition based on facial landmarks and deep learning approaches for visually impaired people. Sensors 23(3):1080

    Article  Google Scholar 

  9. Rajan R, Devasena MG (2022) A novel reading technique for visually impaired person using enhanced optical character recognition method. NEUROQUANTOLOGY 20(12):634–650

    Google Scholar 

  10. Xue K, Barker E (2022) Using artificial intelligence (AI) to improve math accessibility for students with visual impairments. Technical brief. NWEA

    Google Scholar 

  11. Yoon I, Mathur U, Gibson B, Fazli TP, Miele J (2019) Video accessibility for the visually impaired. In: International conference on machine learning AI for social good workshop, vol 1, p 1

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. Vinay .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vinay, M., Jayapriya, J. (2024). Artificial Intelligence-Based L&E-Refiner for Blind Learners. In: Joshi, A., Mahmud, M., Ragel, R.G., Karthik, S. (eds) ICT: Innovation and Computing. ICTCS 2023. Lecture Notes in Networks and Systems, vol 879. Springer, Singapore. https://doi.org/10.1007/978-981-99-9486-1_36

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-9486-1_36

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9485-4

  • Online ISBN: 978-981-99-9486-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics