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It is relatively simple for a normal human to interpret and understand every banknote, but one of the major problems for visually impaired people are money recognition, especially for paper currency. Since money plays such an important role in our everyday lives and is required for every business transaction, real-time detection and recognition of banknotes become a necessity for blind or visually impaired people For that purpose, we propose a real-time object detection system to help visually impaired people in their daily business transactions. Dataset Images of the Iraqi banknote category are collected in different conditions initially and then, these images are augmented with different geometric transformations, to make the system strong. These augmented images are then annotated manually using the "LabelImg" program, from which training sets and validation image sets are prepared. We will use YOLOv3 real-time Object Detection algorithm trained on custom Iraqi banknote dataset for detection and recognition of banknotes. Then the label of the banknotes is identified and then converted into audio by using Google Text to Speech (gTTS), which will be the expected output. The performance of the trained model is evaluated on a test dataset and real-time live video. The test results demonstrate that the proposed method can detect and recognize Iraqi paper money with high mAP reaches 97.405% and a short time.

The usage and existential dependence on banknote withdrawals have increased worldwide. It is actually, to an extent, impossible to perform any transactions without withdrawal of banknotes. The usage of ATM to transact this money has increased worldwide since the last decade too. Though the usage of ATMs’ has significantly increased, it is fairly impossible for the visually challenged to perform ATM transactions without the help of a third-party. Most of the problems arise when the visually challenged request for help or are uncertain of what they do. So, to prevent any security breaches or issues like that, an optical based banknote recognition system can be used. With the help of the system, image recognition can be performed to extract banknote features and Optical Character Recognition can be performed to identify the amount entered, thus, making the entire transaction process easy.

The human hand can copy all that the human hand creates", said Baron von Eichtal in 1843 when he was in charge of banknote issuing for the bank of Bavaria. Despite all the advancements in security printing and anti-counterfeiting, these words ring more true than ever. Indeed in the last thirty years, counterfeiters have greatly benefited from the globalisation of supply chains as well as from the digitization of production methods. Yet the development of digital technologies has also opened unprecedented possibilities for addressing the 3000 years old problem of counterfeiting. This presentation will review some of the counterfeit detection techniques developed in the last 20 years, including printed digital watermarks, copy detection patterns and secure QR Codes. These technologies not only open up the possibility for potentially anyone to verify authenticity with their smartphones, but they also enable the digitization of the trillions of physical documents, packaging and products that we produce every year. Real world examples from industrial applications as well as current research topics will be discussed.

Differentiating between paper currencies with different designs is a challenging task for visually impaired individuals and automated banking machines. Safe and accurate paper currency recognition systems are highly required, because of the wide use of Automated Teller Machines (ATMs), foreign exchange, automatic selling of goods, and automated banking services. With the advances of pattern recognition techniques, many real-life problems have been resolved. This paper presents a robust method to recognize various Yemeni paper currency using pre-trained models. To perform effective recognition processes, deep learning approach is used. Three pre-trained models are implemented which are AlexNet, DenseNet121, and ResNet50. The obtained results of the proposed method achieved high validation accuracy. This robust model reaches a value of 96.99%, 96.83%, and 99.04% in terms of validation accuracy using ResNet-50, DenseNet121, and AlexNet respectively.

Rampant cases related to circulation of fake currency notes have been reported worldwide. With the introduction of new banknotes (announced after demonetization) having advanced and exclusive security features, it is the need of the hour, especially for a forensic Scientists, to have an idea about these new features and the various methods to examine these features. Results presented in this paper are based on examination and comparison of original and suspected fake 2000 rupee Indian currency banknote utilizing physical examination methods and techniques such as physical, microscopic, digital image processing, etc. During the examination of original 2000 rupee banknote, it was found that some security features of this banknote are unique & exclusive and could not be forged by counterfeiters. Counterfeiter could only imitate most of the visual features of banknotes but some specific security features like OVI print, UV-fluorescent print, micro letters, etc. which are placed on all over the substrate of original banknote, could not be mimicked. However, imitation of the visual features, can at times deceives the public in general. The present case study presents a methodology which will be very useful and informative in assisting the forensic community in examination of fake currency banknotes and for future studies.

Automatic Teller Machines (ATMs) and vending machines is a rapidly growing trade in the Iraqi market. Bill acceptors\validators, are considered the heart of these machines have a great significance. Due to the inflation in Iraq with numerous forgery attempt to the currency, an efficient way to accept and validate the Iraqi currency is very important. This paper suggests a bill acceptor\validator to accept, recognize the amount, and validate the bill based solely on image processing functions rather than the magnetic field detection that is used in the regular Bill Acceptors. The program couples the faked bill with a photo graph of the person who presents it. The program uses the Matlab computer vision abilities to recognize the bill. The distinctive characteristics that appears on the banknote when an ultraviolet light is shed on the bill it used for the authentication part with the same Matlab pattern recognition functions. The proposed system include all the mechanical and electronical parts that are used in the proposed system. The results was 100% accurate, which implies that there is great possibility to use this technique in industrial bill acceptors.

According to a survey, approximately 5.4 million people face the problem of visual impairment in the country. Blind people face problems when moving from one place to another many of them use a white cane or stick or a working dog for assistance. When they travel from one place to another, their loved ones are worried about their location. Caretakers or their loved ones find it hard to trace the real-time location of the blind person. Moreover, blind folks face difficulties in contacting their loved ones in case of emergency. They even find it hard to recognize the currencies. Despite the advantages of technology and the enormous usage of electronic cards and e-payments, banknotes are still primarily used owing to its ease. There are about 11 different Indian currencies, and each banknote is distinct from others in various aspects like colour and dimensions. Blind folks find it hard to use the cards and other electronic payments, so they find it hard in recognizing the currencies. Currency Recognition System (CRS) could be useful in identifying currency for visually impaired persons. In the proposed system, the Android-based solution to aid the loved ones to track the real-time location of the blind folks with a smart calling facility and a Currency Recognition System based on Teachable Machine with Google design to recognize various currencies. In Teachable Machine, models are trained using the technique called transfer learning. It aims to provide fast and accurate results for the given image. The goal of this proposed scheme is to make life easier for a visually impaired person.

This paper deals with the matter of identifying the currency that if the given sample of currency is fake. Different traditional strategies and methods are available for fake currency identification based on the colors, width, and serial numbers mentioned. In the advanced age of Computer science and high computational methods, various machine learning algorithms are proposed by image processing that gives 99.9% accuracy for the fake identity of the currency. Detection and recognition methods over the algorithms include entities like color, shape, paper width, image filtering on the note. This paper proposes a method for fake currency recognition using K-Nearest Neighbours followed by image processing. KNN has a high accuracy for small data sets making it desirable to be used for the computer vision task. In this, the banknote authentication dataset has been created with the high computational and mathematical strategies, which give the correct data and information regarding the entities and features related to the currency. Data processing and data Extraction is performed by implementing machine learning algorithms and image processing to acquire the final result and accuracy.

The one important asset of our country is Bank currency and to create discrepancies of money miscreants introduce the fake notes which resembles to original note in the financial market. During demonetization time it is seen that so much of fake currency is floating in market. In general by a human being it is very difficult to identify forged note from the genuine not instead of various parameters designed for identification as many features of forged note are similar to original one. To discriminate between fake bank currency and original note is a challenging task. So, there must be an automated system that will be available in banks or in ATM machines. To design such an automated system there is need to design an efficient algorithm which is able to predict weather the banknote is genuine or forged bank currency as fake notes are designed with high precision. In this paper six supervised machine learning algorithms are applied on dataset available on UCI machine learning repository for detection of Bank currency authentication. To implement this we have applied Support Vector machine, Random Forest, Logistic Regression, Naive Bayes, Decision Tree, K- Nearest Neighbor by considering three train test ratio 80:20, 70:30 and 60:40 and measured their performance on the basis various quantitative analysis parameter like Precision, Accuracy, Recall, MCC, F1-Score and others. And some of SML algorithm are giving 100 % accuracy for particular train test ratio.

Paper currency recognition is an important area of pattern recognition and plays a vital role in day to day life of visually impaired persons. It has various potential applications including electronic banking, currency monitoring systems, money exchange machines, etc. Although there are many potential solutions for paper currency recognition system, those are either logically inefficient or economically infeasible. This paper describes a low cost automated hardware system for currency recognition using color sensors and unsupervised neural networks designed for visually impaired people. This device specifically can be used where paper currency notes have different colors for each class of notes. As a low cost solution, we have used minimal hardware components required to make the system workable. This device reads inputs from banknotes using color sensors and sends data to a trained neural network to predict the category of the note. The experimental results show that the built system is highly accurate.

Almost all fields of life need Banknote. Even particular fields of life require banknotes in large quantities such as banks, transportation companies, and casinos. Therefore Banknotes are an essential component in carrying out all activities every day, especially those related to finance. Through technological advancements such as scanners and copy machine, it can provide the opportunity for anyone to commit a crime. The crime is like a counterfeit banknote. Many people still find it difficult to distinguish between a genuine banknote and counterfeit Banknote, that is because counterfeit Banknote produced have a high degree of resemblance to the genuine Banknote. Based on that background, authors want to do a classification process to distinguish between genuine Banknote and counterfeit Banknote. The classification process use methods Supervised Learning and compares the level of accuracy based on the distribution of training data. The methods of supervised Learning used are Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Naive Bayes. K-NN method is a method that has the highest specificity, sensitivity, and accuracy of the three methods used by the authors both in the training data of 30%, 50%, and 80%. Where in the training data 30% and 50% value specificity: 0.99, sensitivity: 1.00, accuracy: 0.99. While the 80% training data value specificity: 1.00, sensitivity: 1.00, accuracy: 1.00. This means that the distribution of training data influences the performance of the Supervised Machine Learning algorithm. In the KNN method, the greater the training data, the better the accuracy.

Automatic detection and recognition of banknotes can be a very useful technology for people with visual difficulties and also for the banks itself by providing efficient management for handling different paper currencies. Lightweight models can easily be integrated into any handy IoT based gadgets/devices. This article presents our experiments on several state-of-the-art deep learning methods based on Lightweight Convolutional Neural Network architectures combining with transfer learning. ResNet152v2, MobileNet, and NASNetMobile were used as the base models with two different datasets containing Bangladeshi banknote images. The Bangla Currency dataset has 8000 Bangladeshi banknote images where the Bangla Money dataset consists of 1970 images. The performances of the models were measured using both the datasets and the combination of the two datasets. In order to achieve maximum efficiency, we used various augmentations, hyperparameter tuning, and optimizations techniques. We have achieved maximum test accuracy of 98.88\% on 8000 images dataset using MobileNet, 100\% on the 1970 images dataset using NASNetMobile, and 97.77\% on the combined dataset (9970 images) using MobileNet., 4 pages

Hong Kong Dollar is the main currency used in Hong Kong. Hong Kong banknotes are issued by three commercial banks, namely Hongkong and Shanghai Banking Corporation (HSBC), Standard Chartered Bank, and Bank of China. People can typically differentiate the different denominations of coins and banknotes easily by their characteristics. For visually impaired, they can simply determine the values of coins by tactile sense due to the unique size and shape of each type of coin. However, it will be difficult for visually impaired to differentiate different denominations of different types of banknotes. Although the size of each banknote category is different, and it contains characteristics like high-tactility numeral at top left-hand corner, braille and tactile lines at the bottom left-hand corner of the banknote. These features can be used for recognition when the banknote is pretty new. When the banknote becomes old, the features will be unapparent, so visually impaired are hard to confirm the values of the banknotes. Moreover, cheating in purchasing will be much easier in paying the amounts or collecting the change during monetary transactions. In this paper, a tailor-made intelligent banknote recognition system is developed by applying machine learning with assistive technology for visually impaired to determine the denominations of the banknotes effectively and efficiently. The application can be used in a smartphone with sound and vibration to facilitate the daily monetary consumption of the visually impaired people.

A smart banknote is a physical banknote on a paper or polymer substrate that can communicate with an electronic network. A smart banknote is denominated and has the physical properties of a traditional banknote in size, feel, appearance, and etc. It is not a rigid, plastic card such as may be used in present credit or debit transactions. The purpose of a smart banknote is to act as a hybrid that can function either as a definitive or an electronic instrument, depending upon the immediate need. Such functionality will allow a smart banknote to act as a transitional device between traditional payment systems and electronic and crypto-based payment systems. This paper presents an overview of smart banknotes, including their history to date, their differentiation from paper crypto wallets, and their need to mimic traditional banknotes.

Currency counterfeiting is a serious crime that affects a country's finances. The proposed system will be useful to detect counterfeit banknotes in banking systems. India is facing more serious problems due to the increase in fake notes in the market. To get rid of this problem, various fake note detection methods are available worldwide, but most of them are hardware based and costly. The proposed system focuses on having access to the public to identify counterfeit banknotes. The proposed system can identify the legitimacy of a banknote by checking for specific security features such as watermarks, latent images, security threads, etc. Identification of counterfeit banknotes is done using machine learning techniques. The methodology involves extracting and encoding these security features. Security features are extracted from the input image, feature detection and classification are performed using a support vector machine (SVM). Experimental results shown in the paper shows promising results.

Banknote counterfeiting can potentially undermine the integrity of a currency, by eroding both public and retailer confidence in cash as a method of payment. To thwart such criminal counterfeiting activity, banknote issuing authorities employ a range of overt and covert technologies, most in the form of banknote security features. The development, selection and deployment of such features, is an ongoing process undertaken jointly between the manufacturers of security features, banknote printers and banknote issuing authorities, i.e. Central Banks. This ongoing process helps maintain the integrity of banknotes as a recognised, safe and secure means of payment. While some counterfeit banknotes are seized by police at the point of production or whilst in storage, others are removed from circulation during banknote sorting operations, as part of the ‘cash cycle’. Counterfeit banknotes which are removed from circulation are inevitably contaminated, in terms of finger marks and DNA acquired during handling by both criminals and non-criminals alike. However, encapsulated DNA recovered from between the layers of a composite banknote, is highly likely to belong to a person involved in the manufacturing process and is therefore of far greater evidential value. Such evidence has the potential to identify the criminals involved in counterfeit note production. This research evaluates the investigative potential of recovering and profiling of such encapsulated DNA, primarily regarding specific counterfeit types. Accordingly, the objective of the research is to establish an innovative and reliable method of extracting and profiling encapsulated DNA from counterfeit banknotes

The use of counterfeit banknotes increases crime rates and cripples the economy. New countermeasures are required to stop counterfeiters who use advancing technologies with criminal intent. Many countries started adopting polymer banknotes to replace paper notes, as polymer notes are more durable and have better quality. The research on authenticating such banknotes is of much interest to the forensic investigators. Hyperspectral imaging can be employed to build a spectral library of polymer notes, which can then be used for classification to authenticate these notes. This is however not widely reported and has become a research interest in forensic identification. This paper focuses on the use of hyperspectral imaging on polymer notes to build spectral libraries, using a pushbroom hyperspectral imager which has been previously reported. As an initial study, a spectral library will be built from three arbitrarily chosen regions of interest of five circulated genuine polymer notes. Principal component analysis is used for dimension reduction and to convert the information in the spectral library to principal components. A 99% confidence ellipse is formed around the cluster of principal component scores of each class and then used as classification criteria. The potential of the adopted methodology is demonstrated by the classification of the imaged regions as training samples.

Bank note identification is most important approach based on an image processing method. Many techniques and methods are studies involved in the classification of bank notes from different countries conducted experiments on separated image data sets of each country’s. Deep learning is machine learning technique that analyze & learns the features of original note. The most important aspect is to find more essential features by using neural networks. In the era of big data where for any real world application, large amount of data has to be processed, deep learning is the superior techniques. In this research, we studied bank note of various countries by extracting its features in depth and analysis it using deep learning. Our system recommended a deep learning-based algorithm to detect Forged bank note through general scanners that can be used by persons to prevent personal monetary damages caused by fake bank note.

In this research, the author analyzes the merits of smart device apps and low-tech solutions as examples of assistive technology with identification of banknotes by the visually impaired. In particular, the focus of this research is on the potential of low-tech devices that are often neglected in exploring emerging technology. Applying these devices to DIY assistive technology using ICT sometimes opens up new possibilities. In the latter part of this paper, the author attempts to illustrate such possibilities by developing a Web-based system that enables users to freely create 3D data of banknote identification plates according to individual needs.

Transfer learning introduces the ability to perform deep learning models over a small set of data. This paper investigates the utilization of three fine-tuned Convolutional Neural Networks (CNNs), namely, Alexnet, Googlenet, and Vgg16. Alexnet and Googlenet consider as the state-of-the-art models in deep learning, while Vgg16 preference due to its depth. Each model was fine-tuned, trained, and tested over a dataset contains Bosnian Banknotes (BAM). The dataset covers 11 classes where 10 images were collected through mobile phone camera for each class. Alexnet showed a better performance in terms of completing the training while Vgg16 showed better performance in terms of accuracy as it achieved 100% compared to 95.24% for Alexnet. Googlenet showed less efficient performance by achieving 88.65%.

The development of science and technology brings new tools to improve security features on banknotes as well as methods to verify their authenticity. This development also expands the technical possibilities for money counterfeiting. The paper deals with the experimental examination of selected protective elements - paper and inks used on euro banknotes. The advanced analytical method Raman spectroscopy was used for experimental analysis, as method meets the requirements crucial for forensic examination. The main aim is to obtain characteristic Raman spectra of selected security features that can serve for testing the authenticity of questioned banknotes. The results show an apparent diversity of spectral markers in individual samples and the method suitability for the authentication. A comparison of the results for euro banknotes of the first and the Europa series is discussed in the paper.

The aim of the model proposed is to introduce a new hybrid system that can be used with less effort and requiring very few modifications in the existing structures. The model combines the advantages of both digital currency and paper-based banknote system. It uses QR Coded banknotes, digital wallets and blockchain technology to ensure, confirm and enhance the ownership of the banknotes. This model is intended to be used at the macro level by governmental policies since the money system can only be regulated by them.

Detection of counterfeit banknotes becoming a major issue in today’s scenario, as it is seems to be weighty aspect in terms of socio-economy of every living being. This paper highlights various distinguishing features of currencies of different countries, having an objective to secure their currency less susceptible from forgery. The paper specially focuses on the counterfeit analyses of Indian currency and to validate the analyses, paired sample t-test is done on using MS-Excel 2010 on past 14 years of data, taken from RBI Annual reports.

Counterfeiting and forging currencies is a serious threat to any economy. Even though currency exists as a variation of coins, banknotes, and electronic data, many economies remain threatened by counterfeiting which is made possible by the ongoing technological advancements in reprographic equipment available to the general public. Clearly, counterfeit currency detection is not a task that can be neglected. Digital image processing is one of the most common and effective techniques used to distinguish counterfeit banknotes from genuine ones. A new approach is presented in this paper using the bit-plane slicing technique to extract the most significant data from counterfeit banknote images with the application of an edge detector algorithm. The proposed technique consists of decomposing original images of 256 gray levels into their equivalent 8 binary images. This is useful in analyzing the relative importance contributed by each bit of the original image. Higher order bit levels are evaluated for grayscale banknote images with the application of Canny edge detection algorithm. The results are then compared with genuine banknotes and with other existing techniques used for detecting counterfeit notes. Unlike existing research, it was observed that the edges obtained using bit-plane sliced images are more accurate and can be detected faster than obtaining them from the original image without being sliced. The detection of counterfeit currency was also achieved by following the process of using Canny edge detection, image segmentation, and feature extraction.

The recognition of the banknote serial number, which constitutes important data used for various purposes is one of the important functions of banknote counters. However, traditional character recognition methods are limited in terms of speed and performance of serial number recognition. Therefore, in this paper, we propose a character extraction method based on the aspect ratio of banknotes and a character recognition method based on a convolutional neural network (CNN). For character extraction, de-skewing was performed first. Then, the serial number was estimated on the basis of the aspect ratio of the banknote. Further, we designed four types of CNN-based neural networks for character recognition and adopted the most appropriate neural network. Subsequently, we confirmed that the average recognition performance per character for each neural network was 99.85%.

The counterfeit banknote is a growing threat to the society since the advancements in the field of computers, scanners and photocopiers, as they have made the duplication process for banknote much simpler. The fake note detection systems developed so far have many drawbacks such as high cost, poor accuracy, unavailability, lack of user-friendliness and lower effectiveness. One possible solution to this problem could be the use of a system uniquely linked to the banknote itself. In this paper, we present a unique identification and authentication process for the banknote using chaotic elements embedded in it. A chaotic element means that the physical elements are formed from a random process independent from human intervention. The chaotic elements used in this paper are the random distribution patterns of such security fibres set into the paper pulp. A unique ID is generated from the fibre pattern obtained from UV image of the note, which can be verified by any person who receives the banknote to decide whether the banknote is authentic or not. Performance analysis of the system is also studied in this paper.

Banknotes generally have different designs according to their denominations. Thus, if characteristics of each design can be recognized, they can be used for sorting banknotes according to denominations. Portrait in banknotes is one such characteristic that can be used for classification. A sorting system for banknotes can be designed that recognizes portraits in each banknote and sort it accordingly. In this paper, our aim is to automate the configuration of such a sorting system by automatically detect portraits in sample banknotes, so that it can be quickly deployed in a new target country. We use Convolutional Neural Networks to detect portraits in completely new set of banknotes robust to variation in the ways they are shown, such as the size and the orientation of the face.

With the development of Chinese economy, China has become one of the largest countries in terms of foreign trade in the world, and CNY has also ranked second among the currencies all over the world. Thus, the anti-fake and detection technologies for CNY banknotes are also confronted with new challenges. In this paper, ultraviolet-light currency detector and transmission detector, which are two types of simple but practical electronic instrument, are designed by means of the analysis and research on the different optical properties of genuine and fake CNY banknotes respectively. They can be designed with low cost, have a good effect in detection, and may be widespread easily.

Currency identification is the application of systematic methods to determine authenticity of questioned currency. However, identification analysis is a difficult task requiring specially trained examiners, the most important challenge is automating the analysis process reducing human labor and time. In this study, an empirical approach for automated currency identification is formulated and a prototype is developed. A two parts feature vector is defined comprised of color features and texture features. Finally the banknote in question is classified by a Feedforward Neural Network (FNN) and a measurement of the similarity between existing samples and suspect banknote is output.

Advancement of technology over the recent past has led to an increase in circulation of counterfeit notes in today's economy. To combat this issue, it is essential that there exists an efficient mechanism to detect fake banknotes. The main problem with current systems is the trade-off between speed and complexity. This paper proposes a system that can classify and subsequently verify Indian paper currency using fundamental image processing techniques. It uses the comparison between the input banknote and the calculated reference values for different parameters of original banknotes in a similar environment. This system maintains its simplicity while still having high accuracy of 100% for classification and 90% for validity verification.

Currency duplication also known as counterfeit currency is a vulnerable threat on economy. It is now a common phenomenon due to advanced printing and scanning technology. Bangladesh has been facing serious problem by the increasing rate of fake notes in the market. To get rid of this problem various fake note detection methods are available around the world and most of these are hardware based and costly. In the present paper an automated image-based technique is described for the detection of fake banknotes of Bangladesh. Security features of banknotes such as watermark, micro-printing and hologram etc. are extracted from the banknote images and then detection is performed using Support Vector Machine (SVM). Experimental results confirm the effectiveness of the proposed algorithm.

In this study, clustering process has been performed using artificial neural network (ANN) approach on the pictures belonging to our dataset to determine if the banknotes are genuine or counterfeit. Four input parameters, one hidden layer with 10 neurons and one output has been used for the ANN. All of these parameters were real-valued continuous. Data were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. Wavelet Transform tool were used to extract features from images. Four input parameters are processed in the hidden layer with 10 neurons and the output realizes the clustering process. The classification process of 1372 unit data by using ANN approach is sure to be a success as much as the actual data set. The regression results of the clustering process is considerably well. It is determined that the training regression is 0,99914, testing regression is 0,99786 and the validation regression is 0,9953, respectively. Based on the results obtained, it is seen that classification process using ANN is capable of achieving outstanding success.

The paper product in security applications are type of papers designed and manufactured for a specific legal or monetary end-use for an organization or country. The value of the security paper is far beyond the cost of fabrication. This includes checks, bonds, banknotes, stock certificates, visas, passports, etc. This article focuses on currency (banknote) papers, one of the oldest forms of security papers, and how technological changes since 1960 have altered the course of the industry and determines the requirements of future security papers. Based on that, this study describes the end-use and history, properties, raw materials, manufacturing, finishing operations including printing, digital counterfeiting, machine-readable features and the future trends.

In the event of a new version of financial markets, realistic counterfeit money, and only rely on manufacturers to upgrade the software in order to re-identify the class counter counterfeit money, proposed to establish a banknote counter self-learning system. The article mainly describes the specific implementation method of banknote counter being on line self-learning ability, through establishing feature vector template database of banknotes, using the template matching algorithm to identify genuine and fake of banknotes. Experiments show that the smart banknote counter designed in the method has self-learning abilities for fake banknotes, can implement specific anti-counterfeiting, being greatly increase accuracy of identification falsity.

Counterfeiting is an exhaustive problem smashing extensively, virtually as well as in reality, on each sector all around the world. In order to identify and classify fake and genuine banknote various techniques and models have been proposed and developed. This paper proposes an effective predictive model based on machine learning technique for authentication of banknotes, which can predicts with good accuracy that whether the given banknote is fake or genuine. The decision tree model is built using IBM SPSS tool. The performance measure of the model is done using gain charts and index charts and it is found that proposed decision tree model is good enough for prediction of banknote classification as fake or genuine.

In general, dirty banknotes that have creases or soiled surfaces should be replaced by new banknotes, whereas clean banknotes should be recirculated. Therefore, the accurate classification of banknote fitness when sorting paper currency is an important and challenging task. Most previous research has focused on sensors that used visible, infrared, and ultraviolet light. Furthermore, there was little previous research on the fitness classification for Indian paper currency. Therefore, we propose a new method for classifying the fitness of Indian banknotes, with a one-dimensional line image sensor that uses only visible light. The fitness of banknotes is usually determined by various factors such as soiling, creases, and tears, etc. although we just consider banknote soiling in our research. This research is novel in the following four ways: first, there has been little research conducted on fitness classification for the Indian Rupee using visible-light images. Second, the classification is conducted based on the features extracted from the regions of interest (ROIs), which contain little texture. Third, 1-level discrete wavelet transformation (DWT) is used to extract the features for discriminating between fit and unfit banknotes. Fourth, the optimal DWT features that represent the fitness and unfitness of banknotes are selected based on linear regression analysis with ground-truth data measured by densitometer. In addition, the selected features are used as the inputs to a support vector machine (SVM) for the final classification of banknote fitness. Experimental results showed that our method outperforms other methods.

The recognition and recycling of old banknotes are important issues to be solved urgently in the financial industry. In reality the grades of diversely defaced banknotes to be recycled are quite different. Both the absolute and the relative quantities of samples of old banknote are far less than those of new banknote. For processing such imbalanced data sets, the traditional support vector machine (SVM) algorithm might produce poor classification performance for the minority class. To solve this problem, a new method, combining synthetic minority over-sampling technique (SMOTE) with SVM, is put forward to improve the prediction accuracy of old banknotes in this paper. First, a modified SMOTE algorithm is designed to generate new minority class samples. Then, the most easily defaced parts of banknote, the four corners and the center part, are selected as feature areas. Finally, the classification model is obtained using the SVM algorithm. The experimental results show that this method is of high efficiency and the performance can be improved about 20% compared with the method with the standard SVM algorithm.

The document and banknote counterfeiting remove us as a usurpation of the rights of individuals and institutions. By the advancement of technology special paper, the ink usage in the original banknote, placing watermarks, micro text are challenged to fraud that also does not prevent the counterfeiting of widespread. The counterfeit banknotes detection and minimizing damage is one of the important elements. In this, decided and expert systems can be improved to estimate the counterfeit banknotes to using dates of the moneys. The data of banknotes are sorted before to this point, after comparative results are discussed of the testing. In this study, it is classified by the classification algorithms to using digitized available data of real and counterfeit banknotes images. Artificial Neural Networks and Support Vector Machines are used in the classification. Correctly identified is detected to rate of 74.6% in the test results which is tested by Artificial Neural Networks, correctly identified is detected to rate of 93.8% in the results of tests which is tested by Support Vector Machines.

An algorithm for recognizing banknotes is required in many fields, such as banknote-counting machines and automatic teller machines (ATM). Due to the size and cost limitations of banknote-counting machines and ATMs, the banknote image is usually captured by a one-dimensional (line) sensor instead of a conventional two-dimensional (area) sensor. Because the banknote image is captured by the line sensor while it is moved at fast speed through the rollers inside the banknote-counting machine or ATM, misalignment, geometric distortion, and non-uniform illumination of the captured images frequently occur, which degrades the banknote recognition accuracy. To overcome these problems, we propose a new method for recognizing banknotes. The experimental results using two-fold cross-validation for 61,240 United States dollar (USD) images show that the pre-classification error rate is 0%, and the average error rate for the final recognition of the USD banknotes is 0.114%.

Banknotes are currencies used by any nation to carry-out financial activities and are every countries asset which every nation wants it (bank-note) to be genuine. Lot of miscreants induces fake notes into the market which resemble exactly the original note. Hence, there is a need for an efficient authentication system which predicts accurately whether the given note is genuine or not. Exhaustive experiments have been conducted using different machine learning techniques and found that Decision tree and MLP techniques are effective for banknote authentication which efficiently classifies a given banknote data. The rules given by Decision Tree are also tested and found that they are accurate enough to be used for prediction.

With the new, state-of-the-art printing devices and equipment, there has been rapid growth in the counterfeiting of banknotes. Traditional security features on banknotes are easy targets for counterfeiters, and they can easily imitate the original banknotes with fake ones. Conventional methods for validating currency require specialized devices for the authentication of banknotes. However, cost and lack of mobility of sophisticated banknote validation devices are big problems for general consumers. Modern digital solutions are attempting to complement the traditional security features through embedding radio frequency identification (RFID) chips in the banknotes, for example, Euro currency. Unfortunately, the requirement of specialized RFID readers for banknote validation impedes their widespread proliferation among consumers. To overcome this problem, a new method of banknote validation using an RFID chip and an NFC-enabled smartphone is presented. The consumer sends a banknote validation request to the Monetary Agency (MA) using her or his smartphone and an Internet connection. TheMAreplies by sending a random challenge to the consumer’s smartphone. The RFID chip in the banknote receives the challenge, via the NFC, and calculates an equivalent response to theMA’s challenge. If any of the messages are incorrect, authentication is denied. By the proposed method, consumers can easily and instantly check the originality of currency notes with theMAusing their smartphones and an Internet connection. The proposed system is less expensive, computationally, than regular methods and preserves the privacy of people who carry banknotes.

The improvement of reliability in banknote neuro-classifier is investigated and a reject option is proposed based on the probability density function of the input data. The classification reliability is evaluated through two reliability parameters, which are associated with the winning class probability and the second maximal probability. Then a threshold value is set up to reject the unreliable classifications. As for modeling the non-linear correlation among the data variables and extracting the features, a local principal components analysis (PCA) is applied. The method is tested with a learning vector quantization (LVQ) classifier using 1440 data samples of various US dollar bills. The results show that by taking a suitable reject threshold value and also a proper number of regions for the local PCA, the reliability of system can be improved significantly.

Automated currency validation requires a decision to be made regarding the authenticity of a banknote presented to the validation system. This decision often has to be made with little or no information regarding the characteristics of possible counterfeits as is the case for issues of new currency. A method for automated currency validation is presented which segments the whole banknote into different regions, builds individual classifiers on each region and then combines a small subset of the region specific classifiers to provide an overall decision. The segmentation and combination of region specific classifiers to provide optimized false positive and false negative rates is achieved by employing a genetic algorithm. Experiments based on high value notes of Sterling currency were carried out to assess the effectiveness of the proposed solution.

With the advancement in imaging technologies for scanning and printing, production of counterfeit banknotes has become cheaper, easier, and more common. The proliferation of counterfeit banknotes causes loss to banks, traders, and individuals involved in financial transactions. Hence, it is inevitably needed that efficient and reliable techniques for detection of counterfeit banknotes should be developed. With the availability of powerful smartphones, it has become possible to perform complex computations and image processing related tasks on these phones. In addition to this, smartphone users have increased greatly and numbers continue to increase. This is a great motivating factor for researchers and developers to propose innovative mobile-based solutions. In this study, a novel technique for verification of Pakistani banknotes is developed, targeting smartphones with android platform. The proposed technique is based on statistical features, and surface roughness of a banknote, representing different properties of the banknote, such as paper material, printing ink, paper quality, and surface roughness. The selection of these features is motivated by the X-ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) analysis of genuine and counterfeit banknotes. In this regard, two important areas of the banknote, i.e., serial number and flag portions were considered since these portions showed the maximum difference between genuine and counterfeit banknote. The analysis confirmed that genuine and counterfeit banknotes are very different in terms of the printing process, the ingredients used in preparation of banknotes, and the quality of the paper. After extracting the discriminative set of features, support vector machine is used for classification. The experimental results confirm the high accuracy of the proposed technique.

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.

Australia has spearheaded the worldwide struggle against counterfeiting banknotes over the past 30 years. In the first stage polymer banknotes with transparent windows that contain an optically variable devices (OVDs) were issued. New developments comprise (a) the “self-veryfying banknote” where the interaction of features imprinted in the window e.g., an optical lens, and in another region of the note e.g., microtext only legible under this lens, prove the banknote's authenticity, (b) elaborate inks which for example reveal hidden images under special angles or light intensity, and diffractive OVD elements. Despite the great progress made, the quest for more forgery-proof money remains a continuing effort.