Currency Recognition System Research Articles

Efficient currency recognition and value detection system using image processing

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: image acquisition, image pre-processing, feature extraction, image augmentation, and classification. The system uses several image processing algorithms, including data augmentation to enhance the quality of the input images and extract the relevant features. These tasks involve identifying the denomination of a bank note or coin and determining its value. The experiments' results demonstrate the proposed system's effectiveness in real- world scenarios, which can significantly reduce the time and effort required for currency recognition and value detection. In conclusion, the proposed system achieves high accuracy and robustness in recognizing different currencies, including bank notes and coins, under various lighting conditions and orientations. The system's performance can significantly reduce the time and effort required for currency recognition and value detection, making it suitable for use in financial and retail applications. Future work will focus on improving the system's performance in more challenging scenarios, such as handling damaged or counterfeit currency.

Novel Feature Extraction and Representation for Currency Classification

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, feature extraction, classification, and, lastly, results and analysis. The currency image will be pre-processed in grayscale and split into 100x100 blocks at selected regions of interest (ROI) on the currency. Next, binary matrix image features and representations will be extracted. Lastly, the similarity percentage of the binary matrix will be calculated and compared with all currency image matrices. The highest similarity percentage will be chosen as the currency's origin. The proposed algorithm successfully classified the currency and improved the accuracy of currency classification, achieving a 93.4% accuracy rate from the experimental results. The proposed method could be useful for various applications, including financial institutions, security agencies, and automated currency processing machines.

A Study On Detection Of Counterfeit Money By Image Processing

This system, known as the Currency Recognition System, is utilized all over the globe for the purpose of detecting currency fraud. One can find applications for this framework in financial frameworks, cash-monitoring devices, cash-trade frameworks, and so on. In today's technologically sophisticated world, this framework is crucial, and it ought to be simple to obtain. In this essay, we suggest a method for automatically identifying paper money using a programed we've created. Simple, reliable, and effective describe the method that was used. The methodology is divided into several sections, such as those for preparing the images, extracting the characteristics, and comparing the prepared and test pictures. The output includes the currency's denomination, whether it is a genuine note or a fake, and a conversion of the currency into the currency of another country. User-friendly and well-designed mobile software was produced. A challenging dataset with various constraints was used to determine the performance of the proposed system.

CURRENCY RECOGNITION SYSTEM USING IMAGE PROCESSING

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 their denominations. This system works accurately and also able to quickly identify the currency notes.

Deep Learning Approach for Fake Currency Recognition

In this method, the Automatic fake Currency Recognition System is used to identify fake paper money and determine if it is genuine or not. The current counterfeit issue brought on by demonetization has an impact on the financial system as well as other sectors. This strategy, which is comparatively superior to earlier image processing methods, examines a novel Convolution Neural Network approach for the identification of fake money notes through their images. This approach is based on Deep Learning, which has recently shown outstanding results in image categorization problems. Through the use of an image of the fake money note, this approach can assist both humans and machines in instantly recognizing the note. In this system original and fake notes images are used to perform training and classification operation. The proposed system achieved an accuracy of 99.46% and loss of 0.0033 using CNN algorithm.

Android Based Indian Currency Recognition System Using Transfer Learning Technique

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 requires more attention to declining currency handling. When the banknote is blurred or defaced, it is difficult to identify its currency value. A sophisticated design is included to increase the security of the call. This makes the call recognition task very difficult. For correct currency recognition, it is very important to choose a good function and an appropriate algorithm. One of the main problems that blind people face is the recognition of money, especially cash. In a way, the seemingly weakened people do not think about cash settlement and run into problems related to cash transactions in their daily life. It is a useful treatment for those who are externally weakened. Studies and trials were conducted according to key points, such as watermarks, images printed on money, the value of words and numbers, and the total amount of information gathering that stimulated CNN using Transfer Learning. And the second thought after designing a proper algorithm for Indian Currency Recognition, the problem is to carry the mechanism, which can be a burden or sometimes forgotten. Therefore this design help in a lot way for easier way to recognising the Currency just by not making an extra equipment, but by designing an android app, where it is not needed to carry any extra thing, as it is included in android smart phone, which is used by almost 748 million people in India.

Currency Recognition System Using Image Processing: Libyan Banknote as a Case Study

Banknote recognition in the field of image processing is used to distinguish between banknotes of different countries and determine their values. One of the problems that emerged with this process is the recognization of the banknote. The recognition operations for Myanmar currency denominations comprise six stages: image capturing, preprocessing, target region extraction, number segmentation, currency value recognition, and output with screen displaying and voice. The currency recognition system is implemented by comparing the similarity of each banknote value template via image processing steps in the Matlab environment. To prove the effectiveness of the proposed system, the number of banknote images with two types (new and medium) from each of the four types of denominations were tried

IPCRF: An End-to-End Indian Paper Currency Recognition Framework for Blind and Visually Impaired People

Currency recognition has always been a troublesome task for blind and visually impaired people (BVIP). The problem is more severe in developing countries such as India, where there is still a lack of robust currency recognition systems. BVIP primarily relies on size variations and patterns such as intaglio printings for recognizing the underlying currency denominations. Most of the current Indian legal tenders resemble in size, thus making the identification process more strenuous. Also, the engraved patterns are not as distinctive as BVIP standards, and they fade over time. For an automated paper currency recognition system, issues such as folded or partial views, uneven illumination, and background clutter make it non-trivial and challenging. This paper ventures to present an end-to-end and robust framework for assisting BVIP in recognizing the Indian paper currency denomination. This paper presents a lightweight network, IPCRNet, useful in a resource-constrained environment such as low/medium level smartphones. The proposed network is based on Dense connection, Multi-Dilation, and Depth-wise separable convolution layers. Additionally, we congregated one of the most diversified Indian paper currency image dataset with more than 50,000 images belonging to almost all denominations in circulation. A customized and publically available android application, “Roshni-Currency recognizer”, has also been introduced. The experimental results on multiple datasets demonstrate the superiority of the proposed model. IPCRNet improves the classification accuracy by more than 2% on the proposed dataset compared to the state-of-the-art networks.

Real-time currency recognition on video using AKAZE algorithm

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 using the AKAZE algorithm. The purpose of this study is to compare the SIFT and AKAZE algorithms related to a real-time video data processing to determine the value of F1 and its speed. Based on the experimental results, the AKAZE algorithm is resulting F1 value of 0.97, and the processing speed on each video frame is 0.251 seconds. Then at the same video resolution, the SIFT algorithm results in an F1 value of 0.65 and a speed of 0.305 seconds to process one frame. These results show that the AKAZE algorithm is faster and more accurate in processing video data.

A Survey on Automatic Recognition of Fake Indian Currency Note

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.

IoT Enabled Automated Object Recognition for the Visually Impaired

• This paper simplifies the mobility problems of the users by secure and safe movement in indoor and outdoor environments. • The proposed system assists the visually impaired to recognize objects which the visually impaired cannot identify generally. • The accuracy of the proposed system in object detection and recognition is 99.31% and 98.43% respectively. • The proposed system is developed with the least cost components such that the whole system costs an affordable budget. • The proposed system is relatively less weighty than existing systems; hence, a person can carry the developed system easily. Visual impairments have become one of the most predominant problems for the last few decades. To keep doing their daily tasks, vision-impaired people usually seek help from others. An automated common object and currency recognition system can improve the safe movement and transaction activity of visually impaired people . To develop a system that can identify indoor and outdoor objects, notify the users, and send all information to a remote server repeatedly at a fixed time interval. The proposed system assists the visually impaired to recognize several objects and provides an audio message to aware the user. Four laser sensors are used in the system to detect the objects in the direction of the front, left, right and ground. The proposed system uses Single Shot Detector (SSD) model with MobileNet and Tensorflow-lite to recognize objects along with the currency note in the real-time scenario in both indoor and outdoor environments. Among 375 participants, 82% reacted that the price of the proposed system is reasonable, 13% treated as the cost is moderate and the rest 5% people responded that the cost is relatively high for them. In terms of size and weight, 73% reacted that the size and weight are considerable, 20% treated that the size is not suitable, and weight needs to lessen, and the rest 7% people responded that the system is bulky. Regarding input signal observation, 98% responded that they have heard the sound appropriately and the remaining 2% of individuals missed hearing the signal. This paper represents an IoT-enabled automated object recognition system that simplifies the mobility problems of the visually impaired in indoor and outdoor environments. The overall accuracy of the proposed system in object detection and recognition is 99.31% and 98.43% respectively. In addition, the proposed system sends all processed data to a remote server through IoT.

Implementation of New Indian Paper Currency Recognition and Authentication System using Image Processing Technique

Implementation of New Indian Paper Currency Recognition and Authentication System using Image Processing Technique

Currency Recognition and Calculation System using Machine Learning Techniques

Different currencies are being processed in money exchange shops and banks around the globe on a daily basis, where money exchange and transfer takes place. Identifying different currency is a difficult task and can lead to financial loss. There are approximately 180 currencies being used around the world, and each of them differ in color, size and texture. Thus, to correctly identify different currencies, a currency recognition systems needs to be designed. In this paper, we propose the design of an AlexNet based currency recognition system to recognize different international currency notes. We use 10-fold Cross Validation to obtain the cross-validation results of the AlexNet model. The features for the Alex model is extracted from the images back and front of each currency note. We also explore and implement deep learning models to compare the performance of the AlexNet model.

Fake Indian Currency Recognition System by using MATLAB

Fake Indian Currency Recognition System by using MATLAB

Indian Fake Currency Detection using Image Processing: A Review

Paper currency identification is one of the image processing techniques i.e. clothed to recognize currency of different countries. The paper currencies of different countries are collectively rises ever more. However, the main intention of most of the standard currency recognition systems and machines is on recognizing fake currencies. The features are extracted by using image processing toolbox in MATLAB and preprocessed by reducing the data size in captured image. The expose pluck out is discharged by considering HSV (Hue Saturation Value). The chief is neural network classifier and the next step is recognition. MATLAB is used to evolve this program. The new source of paper currency recognition is pattern recognition. But for currency recognition, converter system is an image processing method which is used to identify currency and transfer it into the other currencies as the users need. The need of currency recognition and converters is accurately to recognize the currencies and transfer the currency immediately into the other currency. This application uses the computing energy in differentiation among different kinds of currencies are differentiated with their suitable class using power computing. Fake note at present plays a key topic for the researchers. The recognition system is composed of two parts. First is the captured image and the second is recognition. Forged currencies recognition is the main aim of the standard paper currency identification system. The most mandatory system is currency identification system and it should be very accurate. The performance of different methods are surveyed to refine the exactness of currency recognition system.

Iraqi Currency Recognition System Using RGB and HSV Color Average

Iraqi Currency Recognition System Using RGB and HSV Color Average

Currency Recognition Using Image Processing

There are around 200+ different currencies used in different countries around the world. The technology of currency recognition aims to search and extract the visible as well as hidden marks on paper currency for efficient classification. Currency Recognition and conversion system is implemented to reduce human power to automatically recognize the amount monetary value of currency and convert it into the other currencies without human supervision. The software interface that we are proposing here could be used for various currencies (we are using four in our project). Many a times, currency notes are blurry or damaged; many of them have complex designs to enhance security. This makes the task of currency recognition very difficult. So it becomes very important to select the right features and proper algorithm for this purpose. The basic requirements for an algorithm to be considered as practically implementable are simplicity, less complexity, high speed and efficiency. Our main aim is to design an easy but efficient algorithm that would be useful for maximum number of currencies, because all currencies have different security features, making it a tough job to design one algorithm that could be used for recognition of all available currencies. Writing different programs for all is also a tedious job. The aim of the project is to recognize the currencies and not authentication

A Review Paper on Currency Recognition System

In this paper, an algorithm based on the frequency domain feature extraction method is discussed for the detection of currency. This method efficiently utilizes the local spatial features in a currency image to recognize it. The entire system is pre-processed for the optimal and efficient implementation of two dimensional discrete wavelet transform (2D DWT) which is used to develop a currency recognition system. A set of coefficient statistical moments are then extracted from the approximate efficient matrix. The extracted features can be used for recognition, classification and retrieval of currency notes. The classification result will facilitate the recognition of fake currency mainly using serial number extraction by implementing OCR. It is found that the proposed method gives superior results. Keywordsextraction, classification, discrete wavelet transform, textural feature, currency recognition, OCR, fake currency, security thread, serial number, RBI marks.

An Intelligent Paper Currency Recognition System

Abstract Paper currency recognition (PCR) is an important area of pattern recognition. A system for the recognition of paper currency is one kind of intelligent system which is a very important need of the current automation systems in the modern world of today. It has various potential applications including electronic banking, currency monitoring systems, money exchange machines, etc. This paper proposes an automatic paper currency recognition system for paper currency. A method of recognizing paper currencies has been introduced. This is based on interesting features and correlation between images. It uses Radial Basis Function Network for classification. The method uses the case of Saudi Arabian paper currency as a model. The method is quite reasonable in terms of accuracy. The system deals with 110 images, 10 of which are tilted with an angle less than 15o. The rest of the currency images consist of mixed including noisy and normal images 50 each. It uses fourth series (1984–2007) of currency issued by Saudi Arabian Monetary Agency (SAMA) as a model currency under consideration. The system produces accuracy of recognition as 95.37%, 91.65%, and 87.5%, for the Normal Non-Tilted Images, Noisy Non-Tilted Images, and Tilted Images respectively. The overall Average Recognition Rate for the data of 110 images is computed as 91.51%. The proposed algorithm is fully automatic and requires no human intervention. The proposed technique produces quite satisfactory results in terms of recognition and efficiency.

A Review on Different Currency Recognition System for Bangladesh India China and Euro Currency

Paper currency recognition is one of the important applications of pattern recognition. This application is used to recognize the currency of different countries. Currency recognition system can be used in many places like Hotels, Shops and Automated Teller Machines etc. The currency recognition system should be able to classify this paper currency to the correct class of paper currencies to which it belongs. This paper represents currency recognition system of different countries using different techniques. The paper represents recognition system of different countries like Bangladesh, China, India and recognition system for Euro currency. Different techniques are used to develop these systems like Bangladeshi Currency Recognition System using Negatively Correlated Neural Network, Bangladeshi Currency Recognition System Using Neural Network with Axis Symmetrical Masks and Chinese Currency Recognition System based on BP (Back Propagation) Neural Network Improved by Gene Algorithm, Chinese Currency Recognition by Neural Network, Chinese Currency Recognition based on LBP (Local Binary Pattern). Indian Currency Recognition System based on Heuristic Analysis and Recognition System for Euro using New Recognition Method. This paper represents currency recognition system of different countries and method used to develop these systems.