Third Eye – A Writing Aid for Visually Impaired People
- 1. Assistant Professor, Department of Electronics & Communication Engineering, Presidency University, Bengaluru, Karnataka India
- 2. Assistant Professor, Department of Electronics and Communication Engineering, Sir M Visvesvaraya Institute of Technology, Bengaluru, Karnataka, India.
- 3. Assistant Professor, Department of Electronics and Communication Engineering, Sir M Visvesvaraya Institute of Technology, Bengaluru, Karnataka, India.
Contributors
Contact person:
- 1. Assistant Professor, Department of Electronics & Communication Engineering, Presidency University, Bengaluru, Karnataka India.
Description
Abstract: In olden eras, Braille technology was developed to eradicate the darkness of visually impaired people (Divyanjan) which made them to gain knowledge for proper interaction with the world. Students who read braille can also write braille. Using a variety of low- or high-tech devices, the trainer of students with visually challenged can use braille translation software, which converts the braille into conventional text and prints it. But recognizing the dots on the braille slate and writing the letters is a difficult task faced by many blind kids. Hence the major scope of this proposed project work is to develop a Smart writing system for blind that overcomes many complications faced by a blind child between the age 3-8 years and also to helps them to read and write a standard alphanumeric characters. Henceforth helping them to read and write like a person without disability. Therefore, the project aims to design a writing and reading system for visual impaired person mainly alphanumeric characters. The proposed idea is implemented on Raspberry Pi 4 model interfaced with Speaker and a display unit. Deep Learning algorithms namely CNN is used to recognize the scribbled handwritten text/digits. Overall Performance analysis is made by using MNIST dataset.
Notes
Files
F68040310622.pdf
Files
(575.5 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:c6ddea676d8cbf65b85aa3ccc2b37d88
|
575.5 kB | Preview Download |
Additional details
Related works
- Is cited by
- Journal article: 2277-3878 (ISSN)
References
- World Health Organization. World Health Statistics 2019. Geneva: WHO 2019
- Pascolini D, Marioƫ SP, Pokharel GP,Global update of available data on visual impairment: "a complication of population based prevalence studies". Ophthalmic Epidemiol 2004;11-67.
- Pooja P. Gundewar and Hemant K. Abhyankar, "AReview on an Obstacle Detection in Navigation of VisuallyImpaired", International Organization of ScientificResearch Journal of Engineering (IOSRJEN), Vol.3, No.1pp. 01-06, January 2013.
- BhuvaneshArasu and SenthilKumaran, "Blind Man"s Artificial EYE An Innovative Idea to Help the Blind", Conference Proceeding of the International Journal ofEngineering Development and Research(IJEDR), SRM University, Kattankulathur, pp.205-207, 2014.
- Amjed S. Al-Fahoum, Heba B. Al-Hmoud, and Ausaila a. Al-Fraihat, "A Smart Infrared Microcontroller-Based Blind Guidance System", Hindawi Transactions on Active and Passive Electronic Components, Vol.3, No.2, pp.1-7, June 2013.
- S.Bharathi, A.Ramesh, S.Vivek and J. Vinoth Kumar,"Effective Navigation for Visually Impaired byWearable Obstacle Avoidance System", InternationalJournal of Power Control Signal and Computation(IJPCSC), Vol.3, No.1, pp. 51-53, January-March 2012.
- Michael Mc Enancy "Finger Reader Is audio reading gadget for Index Finger " in July-2014. learning in a spiking convolutional neural network," International Joint Conference on Neural Networks (IJCNN), 2017, pp. 2023-2030.
- Y. Le Cun, "The MNIST database of handwritten digits,"http://yann. lecun. com/exdb/mnist/, 1998.
- J. Jin, K. Fu, and C. Zhang, "Traffic sign recognition with hinge loss trained convolutional neural networks," IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 5, pp. 1991- 2000, 2014.
- M. Wu and Z. Zhang, "Handwritten digit classification using the mnist data set," Course project CSE802: Pattern Classification & Analysis, 2010.
- Y. Liu and Q. Liu, "Convolutional neural networks with large margin softmax loss function for cognitive load recognition," in 2017 36th Chinese Control Conference (CCC), 2017, pp. 4045-4049: IEEE.
- Character recognition algorithms A A Valke and D G LobovIOP Conf. Series: Journal of Physics: Conf. Series 1210 (2019) 012154 IOP Publishing doi:10.1088/1742-6596/1210/1/012154.
- https://github.com/pygame/pygame.
- B. Zhang and S. N. Srihari, "Fast k-nearest neighbour classification using cluster-based trees," IEEE Transactions on Pattern analysis and machine intelligence, vol. 26, no. 4, pp. 525-528, 2004.
- E. Kussul and T. Baidyk, "Improved method of handwritten digit recognition tested on MNIST database," Image and Vision Computing, vol. 22, no. 12, pp. 971-981, 2004.
- R. B. Arif, M. A. B. Siddique, M. M. R. Khan, and M. R. Oishe, "Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Convolutional Neural Network," in 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT), 2018, pp. 112-117: IEEE.
Subjects
- ISSN: 2277-3878 (Online)
- https://portal.issn.org/resource/ISSN/2277-3878
- Retrieval Number:100.1/ijrte.F68040310622
- https://www.ijrte.org/portfolio-item/f68040310622/
- Journal Website: www.ijrte.org
- https://www.ijrte.org/
- Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
- https://www.blueeyesintelligence.org/