QR Denoising using a Hopfield Network
R.Rathi1, Brindha.K2, Sudha.S3, M.Angulakshi4, Deepa.M5
1R. Rathi, working as Assistant professor(senior), School of Information Technology and Engineering, VIT, Vellore, India. K.Brindha, working as Associate professor, School of Information Technology and Engineering, VIT, Vellore, India.
2S.Sudha, working as Associate professor, School of Information Technology and Engineering, VIT, Vellore, India.
3M.Angulakshmi, working as Assistant professor(senior), School of Information Technology and Engineering, VIT, Vellore, India.
4M.Deepa, working as Associate professor, School of Information Technology and Engineering, VIT, Vellore, India.
Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 4934-4938 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6062018520/2020©BEIESP | DOI: 10.35940/ijrte.E6062.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this research paper, we will train and test the Hopfield neural network for recognizing QR codes. We propose an algorithm for denoising QR codes using the concept of parallel Hopfield neural network. One of the biggest drawbacks of the noisy QR code is its poor performance and low storage capacity. Using Hopfield we can easily denoise the QR code and thereby increasing the storage capacity.
Keywords: QR Code, Hopfield Network, Noisy QR Code, Denoising.
Scope of the Article: Distributed Sensor Networks.