Currency Detection Research Articles

Detection of Fake Currency using Machine Learning

Abstract: The problem of detecting fake currency notes is crucial for maintaining the integrity of the economy. In recent years, there has been a surge in the use of deep learning models for detecting counterfeit currency using image processing. For Human being it is very difficult to identify fake currencies, So automatic systems for detection of fake currency is important. In this project, we propose a Convolutional Neural Network (CNN) model for detecting fake currency notes. To train our model, we use a dataset of images containing both genuine and fake currency notes of different denominations. The dataset is preprocessed by resizing all images to a fixed size and normalizing the pixel values. The pre-processed images are then split into training and validation sets for training and testing the model, respectively. This project is modelled as a CNN for automatic feature extraction and classification. The Experimental results validate that the proposed model effectively recognises a real and counterfeit currencies of various denominations with the confidence score.

Counterfeit Detection using Deep Learning

Detecting counterfeit currency is a critical issue that has gained increasing attention in recent years. Deep learning techniques have shown significant promise in a variety of image processing tasks, including counterfeit currency detection. In this project, we propose a deep learning-based counterfeit currency detection system that uses convolutional neural networks (CNNs) for feature extraction and classification. The proposed system consists of two main phases: training and testing. In the training phase, a dataset of genuine and counterfeit banknotes is used to train the CNN model to distinguish between genuine and counterfeit banknotes. A CNN model extracts features from an input image using convolutional layers and then applies fully connected layers for classification. Experimental results show that the proposed deep learning-based counterfeit currency detection system achieves high accuracy. The system outperforms existing state-of-the-art techniques in terms of detection accuracy, robustness, and real-time performance. In conclusion, it can be said that the proposed system could be a useful tool for preventing the circulation of counterfeit currency and minimizing economic losses. The system can be integrated into various automated teller machines (ATMs) and vending machines to detect counterfeit money in real time

Fake Currency Detection Using Image Processing and Random Forest Algorithm

It became relatively easy to make counterfeit currency as technology advanced, such as colour printing. Counterfeit notes are produced so precisely that it's impossible to tell the difference between a genuine and a fake. We can define counterfeit currency notes as copies of actual currency notes produced without the approval of the state or central authority and used to conduct criminal activities. A parallel economy is run with the help of this fake currency note. This parallel economy causes a country's economy to deteriorate, lowering the value of its currency in the global market. As a result, it became critical to build a comprehensive technique to detect counterfeit notes to decrease the flow of counterfeit notes into the market. The fake currency detection system for Indian cash using image processing and machine learning is discussed in this research study. In order to identify real currency notes based on the distinctive features seen on the Indian currency note of 100 rupees, this research uses image processing and the random forest approach. While omitting the texture and colour of the note, banknote identification is accomplished by comparing received banknotes to a database of previously learned images. On the sample size of 400 notes, we found accuracy of 84.25%, recall of 66.25%, and precision of 78.63%. The recommended solution outperforms convolutional neural networks in terms of accuracy and training speed while using a simpler methodology. To prevent inflation and economic loss, this approach can also be used to different types of currency notes.

A Review on Designing of Fake Currency Detection System

The production of duplicate and fake currency has increased with a tremendous amount with the advancement in color print technology. Fake currency is a threat which cannot be easily recognized. Currency detection is a critical problem in various applications such as ATMs, vending machine and currency detection machines. With the increasing globalization of the world economy there is growing demand for accurate and reliable currency detection systems. The proposed system has practical applications in areas such as financial transactions, currency exchange and International trade. It is designed to be fast, accurate and scalable, capable of handling a wide range of currencies. Our research paper introduces a currency detection project that utilizes computer vision techniques to detect and classify banknotes. The proposed system uses image processing and machine learning algorithm to recognize the currency denomination and authenticity. The dataset for training and testing the proposed system is collected and labeled to ensure the accuracy and reliability of the system. Practical outcomes prove that this system aims high precision rates for both currency denomination and authenticity detection, making it an efficient and reliable solution for currency detection. Keywords: Currency Detection, Demonetization, Image Processing, features, System.

Currency Detection and Recognition System Based on Deep Learning

-This paper suggests a method for recognizing currency through image analysis. The method relies on the three characteristics of color, height, and texture, which utilized for distinction. The method put forth in this document can be used to find the currencies of numerous nations. For the purposes of execution, only paper currencies from India are taken into account. This system, which employs the CNN (Convolution Neural Network), makes it simpler to check currency anywhere, at any moment. Every denomination of India was used in our testing, and the system was able to identify with 95% precision. To improve the technique's accuracy, a classification model is created using all of the previously stated factors. Features of paper money are crucial to this identification. Key Words: Currency recognition, color, CNN, Open CV, ResNet.

DeepCyberDetect: Hybrid AI for Counterfeit Currency Detection with GAN-CNN-RNN using African Buffalo Optimization

DeepCyberDetect: Hybrid AI for Counterfeit Currency Detection with GAN-CNN-RNN using African Buffalo Optimization

Recent Advances in Counterfeit Art, Document, Photo, Hologram, and Currency Detection Using Hyperspectral Imaging.

Forgery and tampering continue to provide unnecessary economic burdens. Although new anti-forgery and counterfeiting technologies arise, they inadvertently lead to the sophistication of forgery techniques over time, to a point where detection is no longer viable without technological aid. Among the various optical techniques, one of the recently used techniques to detect counterfeit products is HSI, which captures a range of electromagnetic data. To aid in the further exploration and eventual application of the technique, this study categorizes and summarizes existing related studies on hyperspectral imaging and creates a mini meta-analysis of this stream of literature. The literature review has been classified based on the product HSI has used in counterfeit documents, photos, holograms, artwork, and currency detection.

Vision Based Banknote Recognition System

Abstract: People who are visually impaired face great difficulties in recognizing banknotes for day-to-day transactions as the texture and shape of many banknotes are very similar. Also, the Indian currency system does not have a separate banknote in circulation that might help people who are visually disabled. This paper presents a real-time Indian currency detection system for visually impaired persons. The proposed work makes use of the vision-based system to facilitate the visually impaired people to prosperously recognize banknotes. In this paper, a recognition system is implemented by making use of various machine learning and deep learning approaches which can produce an accuracy of up to 90%. In this system, totally two experiments are carried out on the banknote’s image. The first one is the analysis of the different regions of the banknote, the second experiment is the examination of the banknote image which was captured using a camera in a regulated environment. Our system is extended to support the visually impaired people by executing a “Voice-based assistance system” which would help us in giving the voice output. This system can identify all the seven banknotes used in India and exhibit the result with both textual and auditory output. People who are visually impaired will be able to easily use it in daily transactions.

Foldscope: Increasing Science Accessibility Worldwide

Abstract:Foldscope Instruments, Inc. creates and distributes low-cost, high-quality scientific tools to communities around the world to help advance education, research, and medical diagnostics. The Foldscope—a high-quality microscope created for less than a dollar in parts—was created in 2015. To date, the Foldscope has reached 1.6 million people worldwide in over 160 countries. A few notable uses for the Foldscope include identification of microscopic eggs of agricultural pests in India, creation of a catalog of the biodiversity of soil arthropods in the Amazon, detection of fake currency and medicine, following toxic blooms, detection of bacteria in water samples, and mapping of pollen diversity in a city landscape.

Counterfeit Currency Detection based on AI

Abstract: The use of technology has grown tremendously within the few years it has made it easier to have access to advanced printing equipment in the industry which resulted in color printing of currencies to produce counterfeit notes across the country. To eliminate such unethical activities of printing counterfeit currency it is mandatory to make a system that detects the fake currency, In systems such as a money exchanger for example ATMs and vending machines, counterfeit currency notes must be detected beforehand exchanging process takes place. In the past, there have been similar systems developed based on methods such as image processing techniques that are done on the Matlab platform and other such platforms these methods possess some limitations including being less efficient and time-consuming. Our system is designed to eliminate all of the above problems through the use of deep learning techniques by detecting the features of currencies and determining whether its fake with a great accuracy rate. our proposed system verifies the Indian currency notes using Deep learning, deep learning helps in extracting meaningful information from the dataset fed into the machine using a set of methods to perform the classification of images. Our project makes use of the deep learning framework TensorFlow and its high-level API Keras which simplifies the creation of the model making it easier to achieve a less time-consuming and accurate model. Index Terms: Currency Detection, Deep Learning, Deep Neural Network, Convolution Neural Network

System for Fake Currency Detection Using Image Processing

Abstract: Counterfeit money has always existed an issue that has caused many problems in the market. Technological growth development has made it possible to create extra counterfeit items which are distributed in the mitigation market the global economy. Bangui existing banking equipment and so on trading sites to check the authenticity of funds. But the average person does not do that have access to such systems and that is why they are needed in order for the software to receive counterfeit money, which can be used by ordinary people. This the proposed system uses image processing to find out if the money is real or fake. System built uses the Python system completely language. It contains similar steps grayscale modification, edge detection, separation, etc. made using appropriate methods. Keyword: Counterfeit currency, Image Processing, Python programming language, grayscale conversion, edge detection, segmentation.

A Review on Fake currency detection using feature extraction

A Review on Fake currency detection using feature extraction

Indian Currency Detection Using KNN Classifier

Fake Currency Detection is the biggest problem faced by many countries including India. The advancement of colour printing technology has increased the rate of fake currency note printing and duplicating the notes on a very large scale. So, it has become a necessity to develop a tool that detects the fake currency note in a less time and in a more efficient manner using Image processing.

Detection of Fake Indian Currency Notes using Deep Learning

A normal human being can easily see and distinguish any banknote, however doing the same job is extremely difficult for someone who is visually challenged or blind. Because money plays such an essential part in our everyday lives and is required for any commercial transaction, real-time detection and recognition of banknotes is a must for anyone who is blind or visually impaired. The mobilenet based CNN model-based Indian currency detection and identification system is presented for this purpose, and it is quick and accurate. To make the system more resilient, pictures of various denominations and situations were collected first, and then these images were supplemented with various geometric and image modifications. These augmented pictures are then manually tagged, and training and validation image sets are created from them. Later, the trained model's performance was assessed on a real-time scenario as well as a test dataset. The suggested mobile net model-based technique exhibits detection accuracy of 91.33% according to the test results. This standalone system operates in real-time.

Detection of Fake Currency using Image Processing

In its definition, the term 'currency' defines an agreed-upon exchange item, the national currency being the legal entity used by the selected controlling entity. Throughout history, issuers have faced 1 common threat: counterfeit. In recent years fake money note has been printed that has resulted in significant losses and damage to society. Therefore, it becomes necessary to build a tool for earning money. This research project proposes a way to look at the note of counterfeit money distributed in our country through their image. After selecting an image use pre-processing. In pre-processing, the acquired image is cropped, smooth, and adjust. Change the image to grey-scale. After conversion use image separation. Features are extracted and reduce. Finally, compare the picture to be real or fake. Duplicate money has been a major problem in the market. There are currency counting machines available in banks and other trading venues to check financial authenticity. Most people do not have access to such programs which is why there is a need for fake money laundering software, which can be used by ordinary people. This proposed framework uses Image Processing to determine whether the money is real or counterfeit. The research project program is built entirely using Python's programming language. It has the methods such as grayscale conversion, edge detection, segmentation, etc.

Detection of Counterfeit Indian Currency

Lot of the fake currency note is being printed in recent years which has caused great loss and damage to society. So, it has become necessary to develop a tool to detect fake currency. This project proposes an approach that will detect fake currency notes being circulated by using their image. Our project will provide required portability and compatibility to most peoples as well as feasible accuracy for fake currency detection. The paper is about Fake Indian Paper Currency using image processing implemented in Android Studio to make the app portable and efficient. Features of currency notes like color, height, width, ratio, watermarks were extracted. The process starts from capturing or browsing the image of a currency note and then compare its features with the real note and check whether it is fake or original.

Currency Recognition App

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 and tell the user by speech how much the money note is. This Android project uses speech to text conversion to convert the command given by the blind patient. Speech Recognition is a technology that allows users to provide spoken input into the systems. This android application uses text to speech concept to read the value of note to the user and then it converts the text value into speech. For currency detection, this application uses Azure custom vision API using Machine learning classification technique to detect currency based on images or paper using mobile camera.

Currency Detection for Visually Impaired Iraqi Banknote as a Study Case

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.

Ensemble Deep Learning for Brazil Currency Coin Prediction

In this present fast growing environment the automatic coin reorganization and identification machines has a vital role in all financial allied fields. At present most of coin recognition techniques are depends on physical properties of the coin like length, width, weight etc. Whereas image processing techniques are based on extraction of colour of the coin, edge features of the coin and shape of the coin. For recognition and detection of Brazil currency we have designed Machine Learning, Deep Learning (DL) models in this paper. We have designed various a Machine Learning (ML) models with Convolutional Neural Network for identification and reorganization of Brazil currency. Brazil currency consisting of 5 Centavos, 10 Centavos, 25 Centavos etc. Each of which has different shapes and designs. Different deep learning models are designed and applied ensemble to find the better accuracy. The trained ensemble model is tested on various the datasets which consists of shifting of images, rotation and translated images.

Deep Learning Based Indian Currency Detection for Visually Challenged using VGG16

Banknote recognition is a major problem faced by visually Challenged people. So we propose a system to help the visually Challenged people to identify the different types of Indian currencies through deep learning technique. In our proposed project, bank notes with different positions are directly fed into VGG 16, a pretrained model of convolution neural network which extracts deep features. From our work the visually impaired people will be able to recognize different types if Indian Currencies.