A Sign Language Based ATM Accessing For Blind
DOI:
https://doi.org/10.5281/zenodo.6026696Keywords:
Sign Language, Gestures, Microcontroller, Survey, Blind, ATMsAbstract
The Blind people often have difficulty accessing ATM machines to withdraw money and have to go to the bank. It wastes their time, which makes them uncomfortable. To overcome this problem, most ATM keypads provide Braille embossed keys with the intention of guiding a blind person to access the system. Although useful, Braille unknown blind people fail to access these systems independently. Therefore, to overcome this problem, other ways to access ATM machines should be explored. Sign language is a way of using gestures to communicate with a system in a secure environment. This paper surveys the various methods proposed for sign language recognition.
Downloads
References
Ebey Abraham, Akshatha Nayak and Ashna Iqbal “Real-Time Translation of Indian Sign Language using LSTM” 2019 Global Conference for Advancement in Technology (GCAT)Bangalore, India. Oct 18-20, 2019.
K. SASIREKHA*, M. NIVETHA, A. INDUMATHI and D. RENUKADEVI “ATM Machine For Blind People”.
Drish Mali, Rubash Mali, Sushila Sipai and Sanjeeb Prasad Panday (PhD)”Two Dimensional (2D) Convolutional Neural Network for Nepali Sign Language Recognition” 978-1-5386-9141- bchs0/18/$31.00 ©2018 IEEE.
Yuichiro Mori and Masahiko Toyonaga “Data-Glove for Japanese Sign Language Training System with Gyro-Sensor “2018 joint 10th conference and intelligent system and 19th International Symposism.
Yuna Okayasu,Tatasunori and Maitai Dalhan “Performance Enhancement By Combining Visual Clues To Identify Sign Language Motion” IEEE 2017.
Abdulla Eqab, Tamer Shanableh “Android Mobile App for Real-Time Bilateral Arabic Sign hdbLanguage Translation Using Leap Motion Controller” 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA).
S Yarisha Heera, Madhuri K Murthy,Sravanti V S“Talking Hands” InternationalConference on Innovative Mechanisms for Industry Applications(ICIMIA 2017).
Abhishek B. Jani1 (Member, IEEE), Nishith A. Kotak (Member, IEEE) and Anil K. Roy(Senior Member, IEEE)”Sensor Based Hand Guesture Recognition System for English Alphabets used is Sign Language of Deaf Mute People” 978-1-5386-4707-3/18/$31.00 ©2018 IEEE.
Sanmuk Kaur “Electronic Device Control Using Hand Gesture Recognition System For Differently Abled”.
Nitipon Navaitthiporn , Preeyarat Rithcharung , Phitnaree Hattapath , C. Pintavirooj “Intelligent glove for sign language communication” The 2019 Biomedical Engineering International Conference (BMEiCON- 2019).
Shruthi.G, Sarayu.K.P, Sangeetha.R, Sanjoy Das “Design of ATM Accessing System for blind using real-time video processing through Gestures” International Journal of Computer Applications (0975 –8887) Volume 119 – No.11, June 2015.
Muhammad Shafiq, Jin-Ghoo Choi, Muddesar Iqbal,Muhammad Faheem “Skill specific spoken dialogues based personalized ATM design to maximize effective interaction for visually impaired person” A.Marcus (Ed.): DUXU 2014, Part IV, LNCS 8520, pp. 446–457, 2014.
Meenakshi Panwar “Hand Gesture based Interface for Aiding Visually Impaired” 978-1-4673- 0255-5/12/$31.00c 2012 IEEE.
Paul D. Rosero-Montalvo; Pamela Godoy-Trujillo, Edison Flores-Bosmediano, Jorge Carrascal-Garc?a,Santiago Otero-Potosi, Henry Benitez-Pereira and Diego H. Peluffo- Ordonez “Sign Language Recognition Based on Intelligent Glove Using Machine Learning Techniques” 978-1-5386-6657-9/18/$31.00 c 2018 IEEE.
Neven Saleh, Mostafa Farghaly, Eslam Elshaaer and Amr Mousa “Smart glove-based gestures recognition system for Arabic sign language” 2020 International conference on Innovative trends in communication and Computer Engineering(ITCE2020).
Andrews Samraj and Naser Mehrdel and Shohel Sayeed “Sign Language Communication and Authentication using sensor Fusion of Hand Glove and Photometric Signal” 2017 8th International Conference on Information Technology (ICIT).
Zain Murtaza, Hadia Akmal and Wardah Afzal “Human Computer Interaction based on Gestural Recognition/Sign Language to Text Conversion”.
Dhruva N., Rupanagudi S.R., Neelkant Kashyap H.N. (2013) Novel Algorithm for Image Processing Based Hand Gesture Recognition and Its Application in Security. In: Unnikrishnan S., Surve S., Bhoir D. (eds) Advances in Computing, Communication, and Control. ICAC3 2013. Communications in Computer and Information Science, vol 361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36321-4_51
S. R. Rupanagudi et al., "A high speed algorithm for identifying hand gestures for an ATM input system for the blind," 2015 IEEE Bombay Section Symposium (IBSS), Mumbai, 2015, pp. 1-6, doi: 10.1109/IBSS.2015.7456642.
N. Dhruva, S. R. Rupanagudi, S. K. Sachin, B. Sthuthi, R. Pavithra and Raghavendra, "Novel segmentation algorithm for hand gesture recognition," 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), Kottayam, 2013, pp. 383-388, doi: 10.1109/iMac4s.2013.6526441.
S. S. P G, P. S. Nayak, S. V, S. K, and S. S. G, “Blind Friendly ATM Software System”, pices, vol. 1, no. 4, pp. 36-38, Aug. 2017.
C. V. Reddy, D. M. Ramani, G. K, H. K, and S. B. P, “Gesture Recognition System for the Blind”, pices, no. PaCER 2020, pp. 189-191, Jul. 2020.
Downloads
Published
How to Cite
Issue
Section
URN
License
Copyright (c) 2022 Perspectives in Communication, Embedded-systems and Signal-processing - PiCES
This work is licensed under a Creative Commons Attribution 4.0 International License.
