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Journal articles on the topic 'Currency recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Mahato, Sushil, Satan Kumar Yadav, and Prince Kumar. "Indian Fake Currency Note Recognition." International Journal of Research Publication and Reviews 5, no. 11 (2024): 5403–10. https://doi.org/10.55248/gengpi.5.1124.3340.

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2

Chaudhari, V. J. "Currency Recognition App." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 435–37. http://dx.doi.org/10.22214/ijraset.2021.34982.

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Visually Impaired & foreign people are those people who have vision impairment or vision loss. Problems faced by visually impaired in performing daily activities are in great number. They also face a lot of difficulties in monetary transactions. They are unable to recognize the paper currencies due to similarity of paper texture and size between different categories. This money detector app helps visually impaired patients to recognize and detect money. Using this application blind people can speak and give command to open camera of a smartphone and camera will click picture of the note an

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3

A., S. "Currency Recognition using SIFT." International Journal of Computer Applications 167, no. 9 (2017): 15–20. http://dx.doi.org/10.5120/ijca2017914368.

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4

Padmaja, B., P. Naga Shyam Bhargav, H. Ganga Sagar, B. Diwakar Nayak, and M. Bhushan Rao. "Indian Currency Denomination Recognition and Fake Currency Identification." Journal of Physics: Conference Series 2089, no. 1 (2021): 012008. http://dx.doi.org/10.1088/1742-6596/2089/1/012008.

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Abstract Visually impaired and senior citizens find it difficult to identify different banknotes, driving the need for an automated system to recognize currency notes. This study proposes recognizing Indian currency notes of various denominations using Deep Learning through the CNN model. While not recognizing currency notes is one issue, identifying fake notes is another major issue. Currency counterfeiting is the illegal imitation of currency to deceive its recipient. The current existing methodologies for identifying a phony note rely on hardware. A method completely devoid of hardware that

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5

S.Geetha, P.Yeshika, K.Thapaswi, S.Gnanaharshini, and E.Varshitha. "CURRENCY RECOGNITION SYSTEM USING IMAGE PROCESSING." Journal of Nonlinear Analysis and Optimization 14, no. 02 (2023): 100–107. http://dx.doi.org/10.36893/jnao.2023.v14i2.0100-0107.

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In this paper, we proposed an automatic currency recognition system using digital Image processing methodology. The following project mainly focuses on the recognition of currency by its image or photograph. It will help users to recognize details about currency like Currency Value, Currency Name, the value in INR, EURO, and US Dollar. It works using the main characteristics of currency notes such as size color or printed text on it and also depends on differ in currency notes within the same country. We have considered INDIAN Rupee and US Dollar, the most used currencies in our domain with th

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6

KUMAR, T. RAVI KIRAN, ASVEEN BEGUM, G. BHUVANESHWARI, MD.IBRAHIM, and CH.SATYA TEJA. "CURRENCY RECOGNITION SYSTEM USING IMAGE PROCESSING." Fuzzy Systems and Soft Computing 09, no. 02 (2024): 256–61. https://doi.org/10.36893/fssc.2024.v19i2.036.

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This paper proposes an automatic currency recognition system using a digital image processing methodology. The following project mainly focuses on recognizing currency by its image or photograph. It will help users to recognize details about currency like Currency Value, Currency Name, and the value in INR, EURO, and US dollars. It works using the main characteristics of currency notes such as size color or printed text and also depends on differences in currency notes within the same country. We have considered the INDIAN Rupee and the US Dollar, the most used currencies in our domain with th

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7

Hui Hui Wang, Yin Chai Wang, Bui Lin Wee, and Marcus Chen. "Novel Feature Extraction and Representation for Currency Classification." Journal of Advanced Research in Applied Sciences and Engineering Technology 33, no. 1 (2023): 275–84. http://dx.doi.org/10.37934/araset.33.1.275284.

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In an era marked by the rapidly growing levels of international trade and tourism, the accurate recognition of various currency notes has become a necessity. This paper presents research on an image processing technique for classifying the origin of currencies. Individuals are hardly distinguishing between different currencies from various countries. Therefore, it becomes necessary to develop an automated currency recognition system that helps in recognition notes easily, accurately and efficiency. The methodology consists of five stages, which are image acquisition, image pre-processing, feat

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8

Gupta, Rohit. "Indian Currency Recognition and Authentication." International Journal for Research in Applied Science and Engineering Technology 9, no. 5 (2021): 1021–27. http://dx.doi.org/10.22214/ijraset.2021.34412.

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9

Chhetri, Manoj, and Parshuram Dhungyel. "Bhutanese Currency Recognition using Yolo." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26219.

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In the course of this research, we conducted an examination of Yolo V3 to test its capabilities as a currency recognition model. Accuracy score was used as the evaluation metric and the highest accuracy scored on the testing dataset was 91%. Key Words: Bhutan,currency

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10

Liu, Xi Wen, and Chao Ying Liu. "Paper Currency Image Features Extraction and Recognition Based on Gabor Filter." Applied Mechanics and Materials 635-637 (September 2014): 1030–34. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1030.

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The paper currency image recognition method based on Gabor filter set is discussed in this paper. According to the paper currency image features, the suitable parameters of Gabor filter set are selected for the extraction of paper currency characteristics, the multi-scale and multi-directional texture characteristics of paper currency image are gotten; then the texture images are meshed, and the row and column projection sum of grid pixels' average grey are calculated, finally, the template match method based on grid projection characteristics is used for paper currency recognition. Experiment

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11

Ali, Ahmed, and Mirfa Manzoor. "Recognition System for Pakistani Paper Currency." Research Journal of Applied Sciences, Engineering and Technology 6, no. 16 (2013): 3078–85. http://dx.doi.org/10.19026/rjaset.6.3698.

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12

Singh, Shivam, Anshuman Tiwari, Sameer Shukla, and Sankalp Pateriya. "CURRENCY RECOGNITION SYSTEM USING IMAGE PROCESSING." International Journal of Engineering Applied Sciences and Technology 5, no. 1 (2020): 539–42. http://dx.doi.org/10.33564/ijeast.2020.v05i01.096.

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13

Sarfraz, Muhammad. "An Intelligent Paper Currency Recognition System." Procedia Computer Science 65 (2015): 538–45. http://dx.doi.org/10.1016/j.procs.2015.09.128.

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14

Tiwari, Krishna Kant, and Praveen Dominic. "Currency Recognition System Using Image Processing." International Journal of Computer Science and Engineering 7, no. 6 (2020): 1–3. http://dx.doi.org/10.14445/23488387/ijcse-v7i6p101.

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15

Maddu, Sai Naga Veera Bhadra Rao, and Sai Gowtham Nunna. "Efficient currency recognition and value detection system using image processing." World Journal of Advanced Research and Reviews 22, no. 1 (2024): 241–55. https://doi.org/10.5281/zenodo.14197193.

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In recent years, there has been a growing demand for automated currency recognition and value detection systems to streamline the processes of cash handling and financial transactions. Image processing techniques have emerged as a promising approach to automate these tasks. This paperwork presents an efficient currency recognition and value detection system based on image processing techniques. The proposed system aims to automate the currency recognition and value detection process, which is an essential task in many financial and retail applications. The system consists of several stages: im

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16

Anwar, Hafeez, Farman Ullah, Asif Iqbal, et al. "Invariant Image-Based Currency Denomination Recognition Using Local Entropy and Range Filters." Entropy 21, no. 11 (2019): 1085. http://dx.doi.org/10.3390/e21111085.

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We perform image-based denomination recognition of the Pakistani currency notes. There are a total of seven different denominations in the current series of Pakistani notes. Apart from color and texture, these notes differ from one another mainly due to their aspect ratios. Our aim is to exploit this single feature to attain an image-based recognition that is invariant to the most common image variations found in currency notes images. Among others, the most notable image variations are caused by the difference in positions and in-plane orientations of the currency notes in images. While most

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17

Choudhary, Mr Ratnesh K., Ms Prachi Borate, Mr Pravin Jaiswal, Ms Shweta Gupta, and Mr Vibhanshu Mandaogade. "Literature Survey on Revolutionizing Fake Currency Detection: CNN-Based Approach for Indian Rupee Notes." Journal of Image Processing and Intelligent Remote Sensing, no. 45 (August 30, 2024): 15–24. http://dx.doi.org/10.55529/jipirs.45.15.24.

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The proliferation of counterfeit currency poses a significant challenge in various economies, including India. To address this issue, several studies have proposed innovative image processing and machine learning techniques for detecting counterfeit coins and banknotes. Leveraging digital image processing, these studies aim to enhance the security measures against counterfeit currency through accurate and efficient recognition systems. Techniques such as preprocessing, segmentation, feature extraction, and clustering are employed to identify fraudulent currency. The use of Support Vector Machi

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18

Dhungyel, Parshu Ram, and Manoj Chhetri. "Bhutanese currency recognition using Convolutional Neural Network." Zorig Melong | A Technical Journal of Science, Engineering and Technology 8, no. 1 (2025): 61–67. https://doi.org/10.17102/zmv8.i1.009.

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Currency recognition, or digitization, refers to the process of converting physical currency, such as banknotes and coins, into digital formats. This transformation enhances convenience, security, accessibility, and cost efficiency in financial transactions, which are essential in the modern economy. This paper introduces a Convolutional Neural Network (CNN) model designed for the recognition of Bhutanese paper currency. The model was trained on a dataset comprising various currency types and denominations, achieving a training accuracy of 91 percent and a testing accuracy of 80.5 percent. The

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19

Gour, Minal, Kunal Gajbhiye, Bhagyashree Kumbhare, and M. M. Sharma. "Paper Currency Recognition System Using Characteristics Extraction and Negatively Correlated NN Ensemble." Advanced Materials Research 403-408 (November 2011): 915–19. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.915.

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An efficient currency recognition system is vital for the automation in many sectors such as vending machine, rail way ticket counter, banking system, shopping mall, currency exchange service etc. The paper currency recognition is significant for a number of reasons. a) They become old early than coins; b) The possibility of joining broken currency is greater than that of coin currency; c) Coin currency is restricted to smaller range. This paper discusses a technique for paper currency recognition. Three characteristics of paper currencies are considered here including size, color and texture.

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20

Jyothsna, K. Amrutha. "Currency Classification Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33812.

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Among the uses of machine learning is the recognition of facial expressions. Based on the features that are derived from an image, it assigns a facial expression to one of the classes of facial expressions. Convolutional Neural Network (CNN) is a classification technique that may also be used to identify patterns in an image. We used the CNN approach to identify facial expressions in our proposed study. To increase the precision of facial emotion recognition, the wavelet transform is used after CNN processing. Seven distinct facial expressions are included in the facial expression image datase

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21

Golani, Umesh T. "Android Based Indian Currency Recognition System Using Transfer Learning Technique." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 3197–201. http://dx.doi.org/10.22214/ijraset.2022.45773.

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Abstract: This work is dedicated to develop a computer vision-based approach for Indian paper currency recognition. In this approach, extract currency feature and develop a dataset which can be used for the currency recognition. Security feature of Indian currency note available on front and back side Rs.10, Rs. 20, Rs. 50, Rs. 100, Rs. 200 Rs. 2000 and Rs. 500 denominations are used in model Training. Advances in technology have replaced people in almost every field with machines. Thanks to the introduction of machines, banking automation has reduced the burden on humans. Banking automation r

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22

Parpanathan, Anisha. "A Survey on Automatic Recognition of Fake Indian Currency Note." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 2918–20. http://dx.doi.org/10.22214/ijraset.2021.36980.

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The Currency Recognition System was developed for the purpose of fraud detection in paper currency, so this system is u sed worldwide. The uses of this framework can be recognized in banking frameworks, cash observing gadgets, cash trade frameworks. This paper proposes an automatic paper currency recognition system through an application developed using Machine learning Algorithms. The algorithm implemented is simple, robust and efficient.

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23

Shah, Ami, Komal Vora, and Jay Mehta. "A Review Paper on Currency Recognition System." International Journal of Computer Applications 115, no. 20 (2015): 1–4. http://dx.doi.org/10.5120/20264-2669.

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24

Takeda, Fumiaki, and Sigeru Omatu. "Development of Neuro-Paper Currency Recognition Board." IEEJ Transactions on Electronics, Information and Systems 116, no. 3 (1996): 336–40. http://dx.doi.org/10.1541/ieejeiss1987.116.3_336.

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25

Parpanathan, Anisha. "Automatic Recognition of Fake Indian Currency Note." International Journal for Research in Applied Science and Engineering Technology 9, no. 5 (2021): 1972–74. http://dx.doi.org/10.22214/ijraset.2021.34715.

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26

., Kishan Chakraborty. "RECENT DEVELOPMENTS IN PAPER CURRENCY RECOGNITION SYSTEM." International Journal of Research in Engineering and Technology 02, no. 11 (2013): 222–26. http://dx.doi.org/10.15623/ijret.2013.0211034.

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27

Ibrahim, Dina M., Daliyah S. Aljutaili, Redna A. Almutlaq, and Suha A. Alharbi. "Analysing the steps of currency recognition systems." International Journal of Data Science 5, no. 2 (2020): 143. http://dx.doi.org/10.1504/ijds.2020.10034171.

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28

Alharbi, Suha A., Redna A. Almutlaq, Daliyah S. Aljutaili, and Dina M. Ibrahim. "Analysing the steps of currency recognition systems." International Journal of Data Science 5, no. 2 (2020): 143. http://dx.doi.org/10.1504/ijds.2020.112135.

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29

Amol A. Shirsath, Amol A. Shirsath. "A Review of Paper Currency Recognition System." IOSR Journal of Computer Engineering 10, no. 1 (2013): 71–76. http://dx.doi.org/10.9790/0661-01017176.

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30

Zhang, Qian, Wei Qi Yan, and Mohan Kankanhalli. "Overview of currency recognition using deep learning." Journal of Banking and Financial Technology 3, no. 1 (2019): 59–69. http://dx.doi.org/10.1007/s42786-018-00007-1.

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31

Maddu Sai Naga Veera Bhadra Rao and Nunna Sai Gowtham. "Efficient currency recognition and value detection system using image processing." World Journal of Advanced Research and Reviews 22, no. 1 (2024): 241–55. http://dx.doi.org/10.30574/wjarr.2024.22.1.0949.

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In recent years, there has been a growing demand for automated currency recognition and value detection systems to streamline the processes of cash handling and financial transactions. Image processing techniques have emerged as a promising approach to automate these tasks. This paperwork presents an efficient currency recognition and value detection system based on image processing techniques. The proposed system aims to automate the currency recognition and value detection process, which is an essential task in many financial and retail applications. The system consists of several stages: im

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32

Harjunowibowo, Dewanto, Sri Hartati, and Aris Budianto. "A Counterfeit Paper Currency Recognition System Using LVQ based on UV Light." IJID (International Journal on Informatics for Development) 1, no. 2 (2012): 9. http://dx.doi.org/10.14421/ijid.2012.01202.

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This research is aimed to test a paper currency counterfeit detection system based on Linear Vector Quantization (LVQ) Neural Network. The input image of the system is the dancer object image of paper currency Rp. 50.000,- fluorescent by ultraviolet light. The image of paper currency data was taken from conventional banks. The LVQ method is used to recognize whether the paper currency being tested is counterfeit or not. The coding was carried out using visual programming language. The feature size of the dancer tested object is 114x90 px and the RGBHSI was extracted as the input for LVQ. The e

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33

Joshi, P. P. "CLASSIFICATION MODEL FOR INDIAN CURRENCY USING DEEP LEARNING." International Scientific Journal of Engineering and Management 03, no. 10 (2024): 1–6. http://dx.doi.org/10.55041/isjem01086.

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The field of deep learning has revolutionized computer vision and image recognition, enabling significant advancements in various domains. Image recognition, in particular, has become a prominent use case for deep learning, finding applications in diverse fields for tasks such as image filtering and categorization. One domain where image processing plays a crucial role is the banking sector, where it is used to classify and verify currency notes. In this project, our aim is to propose a machine learning model specifically designed for the classification of Indian currency notes and coins. The

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34

Liu, Hong Hai, and Xiang Hua Hou. "The Correction Algorithm Research of Inclination Paper Currency Image." Advanced Materials Research 756-759 (September 2013): 1464–68. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1464.

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There exist low recognition speed and non-ideal recognition effect in some recognition algorithms of paper currency value. One of very important reasons is that the shooting angle makes the image inclined. This paper firstly analyses the binarization processing of RMB 100-Yuan image and then the method of acquiring straight lines in image is discussed. Thus, the inclination angle of image is calculated by using the obtained straight lines. Finally, through rotation transformation, the inclination image is corrected. The experimental results show that this algorithm has a good corrected effect

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35

Kushwaha, Nidhi, Dr Ashok Verma, and Dr Sharda Patel. "A Review on Recognition of Indian Currency Note Using Cross Co-Relation Technique." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem40141.

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In recent years it has become a very essential to develop an automatic methods for paper currency recognition as its more likely to be used in most of the areas such as vending machines, shopping centers, educational sectors, banking systems in case of huge transactions and so on. As the technology is growing fastly it has become more easy to use such systems. Now a days using automated machines any one can easily get to know whether the currency is a genuine or counterfeit and also the denomination of that currency. The technology has also provided a better way of life for peoples. There is a

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36

Laith, F. Jumma. "Real-Time Recognition and Detection of Iraqi Currency Using DNN." Journal of Scientific Reports 5, no. 1 (2023): 1–7. https://doi.org/10.5281/zenodo.7587998.

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Our daily lives are not possible without money. However, the most crucial issue at this time is how to distinguish between real and fake currencies. The accuracy of cash recognition will be dramatically increased if a computer is used, and the workload of the workforce will be much decreased. It generally uses deep neural networks to learn a dataset. This paper endeavor can make use of a wide variety of models. Accuracy of currency recognition can be increased using these models. Convolutional Neural Networks (CNN) are often quite suitable for our needs regarding money detection. The denominat

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37

Adhinata, Faisal Dharma, Rifki Adhitama, and Alon Jala Tirta Segara. "Real-time currency recognition on video using AKAZE algorithm." Jurnal Teknologi dan Sistem Komputer 9, no. 4 (2021): 191–98. http://dx.doi.org/10.14710/jtsiskom.2021.13970.

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Currency recognition is one of the essential things since everyone in any country must know money. Therefore, computer vision has been developed to recognize currency. One of the currency recognition uses the SIFT algorithm. The recognition results are very accurate, but the processing takes a considerable amount of time, making it impossible to run for real-time data such as video. AKAZE algorithm has been developed for real-time data processing because of its fast computation time to process video data frames. This study proposes the faster real-time currency recognition system on video usin

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38

Gerasimova, Larisa. "Features of payments in foreign currency in budgetary institutions." Buhuchet v zdravoohranenii (Accounting in Healthcare), no. 4 (April 1, 2020): 19–28. http://dx.doi.org/10.33920/med-17-2004-03.

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The article discusses the procedure for accounting for objects in a foreign currency. It is shown that foreign currency assets, liabilities, and other items are recorded simultaneously in foreign currency and in rubles. Analyzed the accounting treatment of exchange rate differences, it is shown that their records depend on the period. Examples of currency monetary and non-monetary accounting items and the specifics of their reflection in accounting transactions are given. Monetary assets and liabilities are recorded at the exchange rate at the date of recognition. The option of recognition at

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Sharma, Bhawani, Amandeep Kaur, and Vipan Vipan. "Recognition of Indian Paper Currency based on LBP." International Journal of Computer Applications 59, no. 1 (2012): 24–27. http://dx.doi.org/10.5120/9514-3913.

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Veling, Mr S. S. "Fake Indian Currency Recognition System by using MATLAB." International Journal for Research in Applied Science and Engineering Technology 7, no. 4 (2019): 3605–12. http://dx.doi.org/10.22214/ijraset.2019.4605.

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Takeda, F., and S. Omatu. "High speed paper currency recognition by neural networks." IEEE Transactions on Neural Networks 6, no. 1 (1995): 73–77. http://dx.doi.org/10.1109/72.363448.

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Hassanpour, Hamid, and Payam M. Farahabadi. "Using Hidden Markov Models for paper currency recognition." Expert Systems with Applications 36, no. 6 (2009): 10105–11. http://dx.doi.org/10.1016/j.eswa.2009.01.057.

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., Syed Ejaz Ali. "CHALLENGES IN INDIAN CURRENCY DENOMINATION RECOGNITION & AUTHENTICATION." International Journal of Research in Engineering and Technology 03, no. 11 (2014): 477–83. http://dx.doi.org/10.15623/ijret.2014.0311082.

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Taranum, Fahmina, Niraja K. S, Shadaan Arzeen, Syeda Zainab Fatima, and Priya Nadimpally. "Assistive technology for currency recognition using deep learning." IET Conference Proceedings 2024, no. 37 (2025): 268–73. https://doi.org/10.1049/icp.2025.0920.

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Gawande, Riya. "Android Based Object Detection System for Visually Impaired." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 1319–27. http://dx.doi.org/10.22214/ijraset.2024.58593.

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Abstract: This review paper presents the design and development of a comprehensive mobile application aimed at enhancing the daily lives of visually impaired individuals. The proposed mobile app offers real-time assistance for various visual recognition tasks, including object detection, distance estimation, currency recognition, barcode detection, color recognition, and emotion analysis. The primary objective of this application is to provide visually impaired users with a powerful tool to navigate their surroundings, identify objects and currency, scan barcodes, discern colors, and even gaug

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Fulsaundar, Prof Priyanka. "Nature as the Fecund Female in Joseph Conrads Heart of Darkness." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 1317–18. http://dx.doi.org/10.22214/ijraset.2023.57574.

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Abstract: The Currency Detection Android Application with Image Processing is an innovative solution designed to empower visually impaired individuals and assist travelers in identifying and managing paper currency notes with accuracy and convenience. This application leverages advanced image processing techniques, including edge detection, color analysis, texture recognition, and optical character recognition (OCR), to extract crucial features from currency notes. Machine learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are employed to reco

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47

Ali Abbasi, Ahmed. "A Review on Different Currency Recognition System for Bangladesh India China and Euro Currency." Research Journal of Applied Sciences, Engineering and Technology 7, no. 8 (2014): 1688–90. http://dx.doi.org/10.19026/rjaset.7.449.

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48

Korennaya, A. A. "DIGITAL CURRENCY AS A SUBJECT OF CRIME AND A MEANS OF COMMITTING CRIMES." Russian-Asian Legal Journal, no. 3 (November 10, 2021): 5–8. http://dx.doi.org/10.14258/ralj(2021)3.1.

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In this article, the author examines the issues of the criminal legal status of digital currency as an objectand as a means of committing a crime. In 2020, a special Federal law was adopted defining the legal status ofdigital assets, as well as amendments were made to the Civil Code of the Russian Federation concerning theestablishment of the legal status of cryptocurrency or digital currency in the terminology of these regulationsas an object of civil rights. Significant changes in the civil legal regulation of cryptocurrencies have led to achange in approaches to assessing the criminal legal

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Mankar, Mrs Shraddha P., Riya Gawande, Dipanshu Sankhala, Siddhi Vispute, and Nikhita Watpal. "Empowering the Blind: An AI Driven Indoor Assistance for Visually Impaired." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 5702–11. http://dx.doi.org/10.22214/ijraset.2024.61273.

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Abstract: This review paper presents the design and development of a comprehensive mobile application aimed at enhancing the daily lives of visually impaired individuals. The proposed mobile app offers real-time assistance for various visual recognition tasks, including object detection, distance estimation, currency recognition, barcode detection, color recognition, and emotion analysis. The primary objective of this application is to provide visually impaired users with a powerful tool to navigate their surroundings, identify objects and currency, scan barcodes, discern colors, and even gaug

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Rucha, Joshi*, and Sarak Kunal. "COLOR IMAGE SEGMENTATION BASED ON MEAN SHIFT AND NORMALIZED CUTS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 4 (2016): 232–35. https://doi.org/10.5281/zenodo.48929.

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An approach for Image segmentation is proposed based on mean shift algorithm and normalized cuts algorithm and its application’s implementation is proposed. The normalized cuts algorithm gives good accuracy and better segmentation compared to all most of the existing methods. By using Mean Shift algorithm on the original image to partition it into sub graphs we can create image matrices with lower dimensions. The proposed algorithm first applied Mean Shift algorithm to obtain sub graphs and then applied Normalized cut. Currency denomination and detection is an application of image segmen

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