A Novel Paper Currency Recognition using Fourier Mellin Transform, Hidden Markov Model and Support Vector Machine

Yaseri, Abbas; Anisheh, Seyed Mahmoud

doi:10.5120/9939-3997

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…Authors of [39] have proposed a system that also identified the Euro currency through the application of image processing steps such as preprocessing and segmentation. After applying the image processing steps, the received value is called a feature vector.…”

Section: Relative Workmentioning

confidence: 99%

A Reliable Technique for Currency Recognition Using Color and Texture Feature Extraction

Saxena,

Singh

2024

Preprint

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Currency is the backbone of the Indian economy. Currency fraud is a serious offense that undermines a country’s financial stability. Because there are more counterfeit currencies on the market, India is dealing with a more serious issue. Currency identification has become a significant area of research nowadays. On November 8, 2016, the Indian government agencies announced the end of circulationof every single 500 and 1,000 rupee notes. There was also an introduction of ₹ 500 and ₹ 2,000 new currencies in exchange for demonetized currencies. After demonetization, there are many fake currencies came into the limelight. There are many fake currency detection approaches available as an alternative, but the majority of them are hardware-based and expensive. These security features are then encoded and fed into machine learning algorithms for feature detection and classification. A reliable approach for currency identification is formulated and also presents a framework for currency recognition based on the texture and color features of a currency, which applies to the Artificial Neural Network (ANN) model. The proposed work’s efficacy across various datasets is shown by experimental results, which also show that recognition performance can be achieved with a range of image sizes.

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Section: Relative Workmentioning

confidence: 99%

A Reliable Technique for Currency Recognition Using Color and Texture Feature Extraction

Saxena,

Singh

2024

Preprint

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“…Furthermore, many researchers have studied the Saudi Arabian currency. Unfortunately, they did not provide information about the currency issue and/or the studied denominations as presented in the following studies: [17][18][19][20][21][22].…”

Section: Related Workmentioning

confidence: 99%

AlFloos: a balanced dataset for the sixth issue of the Saudi Arabian currency banknote*

Bati

2024

J. Umm Al-Qura Univ. Eng.Archit.

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Artificial Intelligence, especially machine learning, is considered by many researchers the new electricity and an important building block of the fourth industrial revolution. Saudi Arabia is amongst the first nations worldwide to realize this importance and apply it in its school systems at various levels. This interest motivates researchers to create Arabic datasets for research and educational purposes, particularly with the popularity of English sources and the absence of Arabic sources. This study attempts to lessen this gap by creating for the first time ever a dataset for the sixth issue of the Saudi Arabian currency and analyzing it using the famous machine learning tools: Orange Data Mining, Google Teachable Machine, and Liner.ai, which require no coding. The importance of the study to the community is as follows: 1—It provides the first work ever to establish a balanced dataset for the sixth issue of the Saudi Arabian currency banknote, which contains images for the banknote denominations and the tabular data generated using deep learning. 2—It is the first scientific work that uses shallow machine learning and deep learning models to create good-performing models for classifying the sixth issue of the Saudi Arabian currency without coding. This work paves the way for researchers and those interested from various fields and backgrounds to develop machine learning applications to classify the sixth issue of the Saudi Arabian currency, especially in mobile phones or in microcontrollers, to inspire multiple IoT and Tiny machine learning applications like currency recognition and classification for special needs people, automatic counting and sorting of banknotes in banks and stores, and fake currency detection.

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“…If a banknote has been in circulation for a long time, it may be difficult to extract accurate banknote recognition features due to its surface being soiled by dirt and sebum from users’ hands. To address this, noise removal is performed as a general preprocessing step using techniques based on the Wiener filter [ 10 , 49 , 55 , 65 , 71 ] or median filter [ 42 , 64 ]. Noise occurring in the imaging process or banknote aging can also be diminished by reducing the gray level of the image beyond the 0–255 range [ 54 , 65 , 71 , 72 ].…”

Section: Banknote Recognitionmentioning

confidence: 99%

“…To address this, noise removal is performed as a general preprocessing step using techniques based on the Wiener filter [ 10 , 49 , 55 , 65 , 71 ] or median filter [ 42 , 64 ]. Noise occurring in the imaging process or banknote aging can also be diminished by reducing the gray level of the image beyond the 0–255 range [ 54 , 65 , 71 , 72 ]. Some studies have presented methods to normalize the brightness and improve the contrast of the image by means of histogram equalization [ 42 , 45 ].…”

Section: Banknote Recognitionmentioning

confidence: 99%

“…Among other classification methods listed in Table 4 , there are simple methods of comparing distances using the Euclidean distance-based classifier [ 36 , 37 , 41 , 42 , 48 , 51 ] or the Mahalanobis distance-based classifier [ 23 ], and complicated methods using sophisticated pattern recognition-based classifiers, such as support vector machines (SVM) [ 8 , 11 , 39 , 43 , 67 , 71 ] or hidden Markov models (HMM) [ 13 , 65 , 71 ]. Furthermore, there are methods using the clustering-based K-means classifier [ 8 , 9 ] and denomination classification methods using K-NN ( k -nearest neighbors) [ 55 , 64 ].…”

Section: Banknote Recognitionmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Banknote Recognition Methods by Various Sensors

Lee

Hong

Kim

et al. 2017

Sensors

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Despite a decrease in the use of currency due to the recent growth in the use of electronic financial transactions, real money transactions remain very important in the global market. While performing transactions with real money, touching and counting notes by hand, is still a common practice in daily life, various types of automated machines, such as ATMs and banknote counters, are essential for large-scale and safe transactions. This paper presents studies that have been conducted in four major areas of research (banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification) in the accurate banknote recognition field by various sensors in such automated machines, and describes the advantages and drawbacks of the methods presented in those studies. While to a limited extent some surveys have been presented in previous studies in the areas of banknote recognition or counterfeit banknote recognition, this paper is the first of its kind to review all four areas. Techniques used in each of the four areas recognize banknote information (denomination, serial number, authenticity, and physical condition) based on image or sensor data, and are actually applied to banknote processing machines across the world. This study also describes the technological challenges faced by such banknote recognition techniques and presents future directions of research to overcome them.

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A Novel Paper Currency Recognition using Fourier Mellin Transform, Hidden Markov Model and Support Vector Machine

Cited by 6 publications

References 10 publications

A Reliable Technique for Currency Recognition Using Color and Texture Feature Extraction

A Reliable Technique for Currency Recognition Using Color and Texture Feature Extraction

AlFloos: a balanced dataset for the sixth issue of the Saudi Arabian currency banknote*

A Survey on Banknote Recognition Methods by Various Sensors

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