FPGA Based Effective Communication System for Physically Challenged People

  • Rajamani A PSG College of Technology
  • Saranya N

Abstract

This paper aims to develop a real time system for deaf and dump people for achieving better way of communication by using hand gesture recognition. Existing way of communication is not much helpful for the deaf and dump people, since existing approach depends upon translator person, who can understand the sign language of the dump and they manually decode their signs and convey the message. The translator may commit some mistakes due to wrong recognition and that limitation could be significantly overcome by using an image acquisition device and processing unit to process the gestures. The personal computer will correctly display the message which was communicated.

References

[1] D. Georganas Nicolas, E. M. Petriu, Qing chen, “Real-time Vision-based Hand Gesture Recognition Using Haar-like Features” in IEEE Instrumentation and Measurement Technology Conference Proceedings, 2007.
[2] Q. Gu, T. Takaki, and I. Ishii, “Fast FPGA-based multiobject feature extraction,” IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 1, pp. 30– 45, Jan. 2013.
[3] A. Aliaa, A.Youssif ,AmalElsayedAboutabl, HebaHamdy Ali “Arabic Sign Language (ArSL) Recognition System Using HMM,” (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 2, no. 11, 2011. [4] Y. C. Ham and Y. Shi, “Developing a smart camera for gesture recognition in HCI applications,” in Proc. 13th IEEE Int. Symp. Consumer Electron. (ISCE), pp. 994–998, May 2009.
[5] HiroomiHikawa and KeishiKaida, “Novel FPGA implementation of hand sign recognition system with SOMHebb classifier,” IEEE Trans. Circuits Syst. Video Technol., vol. 25, no. 1, pp. 153–166, Jan. 2015.
[6] K. Assaleh and M. Al-Rousan, “Recognition of Arabic sign language alphabet using polynomial classifiers,” EURASIPJ. Appl. Signal Process., vol. 2005, pp. 2136–2145, 2005.
[7] A. Buchman, and A. Vida-Ratiu, “Hand postures recognition system using artificial neural networks implemented in FPGA,” in Proc. 30th Int. Spring Seminar Electron. Technol., pp. 507–512, 2007.
[8] M. Mohandes, M. Deriche, and J. Liu, “Image-Based and Sensor-Based Approaches to Arabic Sign Language Recognition,” IEEE Trans. on Human-Machine Systems, vol. 44, no. 4, Aug. 2014.
[9] Feng-Cheng Huang, Shi-Yu Huang “High-Performance SIFT Hardware Accelerator for Real-Time Image Feature Extraction,” IEEE Trans. on Circuits Systems for Video Technology, vol. 22, no. 3, Mar. 2012.
[10] N. Ghasem-Aghaee and S. A. Monadjemi,“Rapid hand posture recognition using adaptive histogram template of skin and hand edge contour,” in Proc. 6th Iranian March.
[11] P. SubhaRajam and Dr. G. Balakrishnan “Real Time Indian Sign Language Recognition [4] System to aid Deaf - dumb People” IEEE International Conference, 2013.
[12] Ibrahim Patel1, Dr. Y. Srinivas Rao2 proposes a “Automated speech synthesizer and converter in cue symbol generation for physically impairs”. International Journal of Recent Trends in Engg, Vol 2, No. 7, Nov. 2009. [13] Soumya Dutta and Bidyut B. Chaudhuri “A Color Edge Detection Algorithm in RGB Color Space” IEEE Transactions Vol.8, No. 5, May 2013.
[14] Anbarasi Rajamohan, Hemavathy R., Dhanalakshmi proposes a” Deaf-Mute Communication Interpreter”. Proceedings of the International MultiConference of Engineers and Computer Scientists, IMECS, Hong Kong, Vol 1, pp. 18 - 20, 2009.
Published
2017-11-08
How to Cite
A, Rajamani; N, Saranya. FPGA Based Effective Communication System for Physically Challenged People. INTERNATIONAL JOURNAL OF ELECTRONICS & DATA COMMUNICATION, [S.l.], v. 5, n. 1, p. 119 - 124, nov. 2017. ISSN 2278-5620. Available at: <http://cirworld.com/index.php/ijedc/article/view/6408>. Date accessed: 21 nov. 2017. doi: https://doi.org/10.24297/ijedc.v5i1.6408.
Section
Articles