Smart Vision Based ATM Transaction using Deep Learning Neural Networks
Kalpana Devi S1, Pranusha A2, Priyanka S3, Ram Abhishek RS4
1Kalpana Devi S, Assistant Professor, CSE Department, Easwari Engineering College, Chennai, Tamil Nadu.
2A Pranusha, CSE Department, Easwari Engineering College, Chennai, Tamil Nadu.
3S Priyanka, CSE Department, Easwari Engineering College, Chennai, Tamil Nadu.
4R S Ram Abhishek, CSE Department, Easwari Engineering College, Chennai, Tamil Nadu.
Manuscript received on February 27, 2020. | Revised Manuscript received on March 14, 2020. | Manuscript published on March 30, 2020. | PP: 5126-5131 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9075038620/2020©BEIESP | DOI: 10.35940/ijrte.F9075.038620
Open Access | Ethics and Policies | Cite | Mendeley
© 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: The necessity of credit cards and online payment techniques become extremely popular and simple to perform because of easy and safe money handling techniques. The usage of the ATM by visually challenged people is a problem. Though there are certain features for the visually challenged users like speech instructions, there is no conformity of the amount entered or of that transacted. As a result, these people have no security, ease or comfort during the ATM transactions. So, there is a need to provide a method for the visually challenged people to effortlessly perform ATM transactions with better security. Our proposed system designed a device that can act as an aid for the visually challenged to transact in the ATM. The devised system recognizes the amount to be transacted as entered on the screen using Optical Character Recognition (OCR) and conveys it to the user via speech. After transaction, the banknotes are recognized by the system using image recognition through vital banknote feature extraction and the verification is provided regarding the amount transacted and the intended amount.
Keywords: ATM Transaction, Optical Character Recognition (OCR), Image Recognition, Banknote Recognition, Feature Extraction.
Scope of the Article: Deep learning