INDIAN BANK CURRENCY RECOGNITON AND FITNESS USING IMAGE PROCESSING
DOI:
https://doi.org/10.61841/xae1by25Keywords:
Genuine, Fake, Currency, GLCM, Dilation, Erosion, Image ProcessingAbstract
To count currency as soon as possible for the bank staff that implementation in the financial organizations, paper recognition and classification system has created as one of the most important applications of pattern in recognition system. Features extraction using the Gray Level Co-occurrence Matrix directly affects the recognition ability. A method and model for automatic classification and recognition of currency notes using a supervised learning classifier is the most important and simplest method in pattern recognition. In this paper, we are going to implement based on textural features such as GLCM. The recognition system is classified into four types. The skew correction of a gray image is first. The captured input gray image is the second preprocessing, and the third method is nothing but extracting its features by using the Gray Level Co-Occurrence Matrix. The recognition system presented that the approach is one of the most effective methods of recognizing currency patterns to read their value.
Downloads
References
1. Venugopal, Vipin, Deborah Thomas, and Arya Prasad. "Indian Currency Recognizer and Counter System." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 4, no. 5 (2014): 34-3.
2. Hassanpour, Hamid, A. Yaseri, and G. Ardeshiri. "Feature extraction for paper currency recognition." In 2007 9th International Symposium on Signal Processing and Its Applications, pp. 1-4. IEEE, 2007..
3. Vishnu, R., and Bini Omman. "Principal features for Indian currency recognition." In 2014 Annual IEEE India Conference (INDICON), pp. 1-8. IEEE, 2014.
4. Yi, Chucai, Yingli Tian, and Aries Arditi. "Portable camera-based assistive text and product label reading from hand-held objects for blind persons." IEEE/ASME Transactions On Mechatronics 19, no. 3 (2013):808-817.
5. Pham, Tuyen Danh, Young Ho Park, Seung Yong Kwon, Kang Ryoung Park, Dae Sik Jeong, and Sungsoo Yoon. "Efficient banknote recognition based on selection of discriminative regions with a one-dimensional visible-light line sensor." Sensors 16, no. 3 (2016): 328.
6. Doush, Iyad Abu, and AL-Btoush Sahar. "Currency recognition using a smartphone: Comparison between color SIFT and grayscale SIFT algorithms." Journal of King Saud University—Computer and Information Sciences 29, no. 4 (2017): 484-492.
7. Jain, Vipin Kumar, and Ritu Vijay. "Indian currency denomination identification using image processing technique." (2013).
8. Reel, Parminder Singh, Gopal Krishan, and Smarti Kotwal. "Image processing-based heuristic analysis for enhanced currency recognition." International Journal of Advancements in Technology 2, no. 1 (2011): 82-89.
9. Sarfraz, Muhammad. "An intelligent paper currency recognition system." Procedia Computer Science 65 (2015): 538-545.
10. Gogoi, Mriganka, Syed Ejaz Ali, and Subra Mukherjee. "Automatic Indian currency denomination recognition system based on artificial neural network." In 2015, 2nd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 553-558. IEEE, 2015.
11. Murthy, Sahana, Jayanta Kurumathur, and B. Roja Reddy. "Design and implementation of paper currency recognition with counterfeit detection." In 2016 Online International Conference on Green Engineering and Technologies (IC-GET), pp. 1-6. IEEE, 2016.
12. Anilkumar, B., and K. R. J. Srikanth. "Design and development of real-time paper currency recognition system of demonetization of New Indian Notes by using Raspberry Pi for visually challenged." Int. J. Mech. Eng. Technol 9 (2018): 884-891.
13. Identification of Counterfeit currency and denomination using Raspberry Pi Swami Gururaj M.1, Naveen J.2 Student, Department of EEE, NMAMIT, Nitte, Udupi, India1 Assistant Professor, Department of EEE, NMAMIT, Nitte, Udupi, India.
14. Mittal, Shubham, and Shiva Mittal. "Indian Banknote Recognition using Convolutional Neural Network." In 2018 3rd International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), pp. 1-6. IEEE, 2018.
15. Pandey, Amit, and Gyan Prakash. "Deduplication with Attribute-Based Encryption in E-Health Care Systems." International Journal of MC Square Scientific Research 11, no. 4 (2019): 16-24.
16. Shahada, Shareefa Ahmad Abu, Suzan Mohammed Hreiji, and Shermin Shamsudheen. "IOT-BASED GARBAGE CLEARANCE ALERT SYSTEM WITH GPS LOCATION USING ARDUINO." International Journal of MC Square Scientific Research 11, no. 1 (2019): 1-8.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 AUTHOR
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
