Coin Recognition Research Articles

Mobile Application for Recognizing Colombian Currency with Audio Feedback for Visually Impaired People

Context: According to the census conducted by the National Department of Statistics (DANE) in 2018, 7.1% of the Colombian population has a visual disability. These people face conditions with limited autonomy, such as the handling of money. In this context, there is a need to create tools to enable the inclusion of visually impaired people in the financial sector, allowing them to make payments and withdrawals in a safe and reliable manner. Method: This work describes the development of a mobile application called CopReader. This application enables the recognition of coins and banknotes of Colombian currency without an Internet connection, by means of convolutional neural network models. CopReader was developed to be used by visually impaired people. It takes a video or photographs, analyzes the input data, estimates the currency value, and uses audio feedback to communicate the result. Results: To validate the functionality of CopReader, integration tests were performed. In addition, precision and recall tests were conducted, considering the YoloV5 and MobileNet architectures, obtaining 95 and 93% for the former model and 99% for the latter. Then, field tests were performed with visually impaired people, obtaining accuracy values of 96%. 90% of the users were satisfied with the application’s functionality. Conclusions: CopReader is a useful tool for recognizing Colombian currency, helping visually impaired people gain to autonomy in handling money.

Fuzzy Power, Converter for Coin Sorting System Based on Weight, Edge

Nowadays, intelligent robot technology is extensively used in every aspect of human existence and production. To achieve automatic sorting, future development must concentrate on employing intelligent robots instead of humans. Coin sorting robots are an important research object in the field of sorting robots. This paper focuses on a machine vision (MV) sorting especially difficult application, in which the `part' is an Indian coin. The lack of distinguishing characteristics between denominations and the variation in features within a single denomination makes the application difficult in part. Even though coin recognition algorithms have been published in the literature, most applications are evaluated offline using the coins' weights. In the end, it was determined that none of these three methods achieved the desired 95% accuracy at 1000 coins per minute.

AUTOMATED COIN RECOGNITION SYSTEM USING ANN

Coins are integral part of our day to day life. We use coins everywhere like grocery store, banks, buses, trains etc. So it becomes a basic need that coins can be sorted and counted automatically. For this it is necessary that coins can be recognized automatically. In this paper we have developed an ANN (Artificial Neural Network) based Automated Coin Recognition System for the recognition of Indian Coins of denomination `1, `2, `5 and `10 with rotation invariance. We have taken images from both sides of coin. So this system is capable of recognizing coins from both sides. Features are extracted from images using techniques of Hough Transformation, Pattern Averaging etc. Then, the extracted features are passed as input to a trained Neural Network. 97.74% recognition rate has been achieved during the experiments i.e. only 2.26% miss recognition, which is quite encouraging.

Smart Vending Machine

Abstract: Now-a-days vending machines are becoming very popular in western countries because of their ease, less wastage of time and effort, availability near doorstep and variety of products. But in India, vending machines are still not so popular according to the customer's requirement. In this paper, a model of vending machine is proposed which dispenses liquid products by accepting the coin. The model of vending machine is customer friendly with a feature of fake coin detection. This machine is implemented using AVR ATmega16 microcontroller. Various modules of the machine are interfaced with the microcontroller. Coin recognition through CS-616 coin acceptor and IR sensor makes this model simple and inexpensive as compared to the previous work where either currency or tokens were used or difficult techniques like image recognition for detection of coin. Keywords: Atmega16,IR sensor, CS-616 Coin acceptor

Fast‐moving coin recognition using deep learning

Deep learning has made essential contributions to the development of visual object detection and recognition. Identifying fast-moving objects from the viewpoint of computer vision remains as a challenging problem. The best solution in deep learning that can well represent the characteristics of object motion is related to recurrent neural network (RNN), the best model for fast-moving object recognition is Long Short-Term Memory (LSTM) in RNN. Therefore, the combination of LSTM and CNN fully utilizes spatial and temporal features of moving objects. In this paper, our goal is to identify fast-moving coins from digital videos by using deep learning methods, especially based on the mixture of LSTM and CNN. By using the proposed method, we gain the attainment with high accuracy of fast-moving coin recognition which is superior to our human visual system.

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.

AUTOMATIC CLUSTERING OF CELTIC COINS BASED ON 3D POINT CLOUD PATTERN ANALYSIS

Abstract. The recognition and clustering of coins which have been struck by the same die is of interest for archeological studies. Nowadays, this work can only be performed by experts and is very tedious. In this paper, we propose a method to automatically cluster dies, based on 3D scans of coins. It is based on three steps: registration, comparison and graph-based clustering. Experimental results on 90 coins coming from a Celtic treasury from the II-Ith century BC show a clustering quality equivalent to expert’s work.

Circle Hough Transform (CHT) method applied to coin identifier and recognition based on computer vision and android developer

The coin recognition system is a very important role in currency processing systems which is aims to minimize human miscalculation and time to calculate coin values. The coin identifier and recognition to calculate the value of Indonesian coins based on android developer presented in this paper. The two coin parameters are used for coin recognition which are radius and colour of the coins. The Circle Hough Transform (CHT) method with gray scaling and erosion process as pre-processing image used to calculate radius parameters, based on android developer and computer vision. After the radius value is obtained then the average RGB value of the coins is detected to determine the colour parameters. By calculating the diameter and determining the colour, the coins can be recognized and the total value of the coins in the input image can be known. The experimental results show that the system has been obtained indicate that the system successfully recognized coins based on determined algorithm. That is mean the application using computer vision can recognition coin and count the value of coins.

Brazilian Coins Recognition Using Histogram of Oriented Gradients Features

This paper tends to the issue of coin recognition when dealing with shading and reflection variations under the same lighting conditions. In order to approach the problem, a database containing Brazilian coin images (both front and reverse side of the coin) consisting of five different denominations have been used which is provided by the kaggle-diverse and largest data community in the world. This work focuses on an automatic image classification process for Brazilian coins. The imagebased classification of coins primarily incorporates three stages where the initial step is Region of Interest (ROI) extraction; the subsequent advance is extraction of features and classification. The first step of ROI extraction is accomplished by segmenting the coin region using the proposed segmentation method. In the second step i.e., feature extraction; Histogram of Oriented Gradients (HOG) features are extracted from the image. The image is converted to a vector containing feature values. The third step is where the extracted features are mapped to the class and are known as classification. Three classification algorithms i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbour are compared for classification of five coin denominations. With the proposed segmentation methodology, the best classification accuracy of 92% is achieved in the case of ANN classifier.

Coin Recognition Approach in Social Environments Using Virtual Organizations of Agents

Social systems have gained relevance during the last decade, trying to provide intelligent environments where humans and machines collaborate to resolve a social problem. The main objective of this paper is to obtain an intelligent system specifically designed to help dependent and/or visually disabled people to count money more easily by using a mobile phone camera. The proposed system incorporates an image recognition system for classifying coins by using homography to transform images previously for classification tasks. The main difficulty in the appliance of these techniques relies on the fact that camera position and height are unknown. This process allows changing the perspective of the images in order to calculate different meaningful variables such as diameter and colour employed later to perform classification and counting tasks. The system uses the information of the variables as inputs for classification algorithms that allow us to identify the amount and type of coins. The system has been tested with euro coins. This paper presents the results obtained.

A hybrid vision-based surface coverage measurement method for robotic inspection

Abstract Vision-based surface coverage measurement serves a large area of applications including robotic shot peening, robotic inspection in the fields of blood cell segmentation, coin recognition systems and many more. Manual visual inspection is time consuming and prone to human error. The objective is to develop a real-time generic algorithm, robust to non-uniform illumination with high accuracy, and having a relatively simple experimental setup. In this paper, a hybrid method based on morphological operations and Boykov graph-cuts segmentation is proposed for vision-based surface coverage measurement. The primary focus is on shot peening; however, the proposed algorithm will be extended towards other applications such as blood cell segmentation and coin recognition. The performance of the proposed algorithm is evaluated experimentally.

An Enhanced Histogram of Oriented Gradient Descriptor for Numismatic Applications

The Histogram of Oriented Gradients (HOG) is one of the most widely used methods to extract the gradient features for object recognition and consistently shows high accuracy rates when compared to other descriptors. The major drawbacks of using the HOG method are the necessity of finding an optimal window size to fit the whole object; and the exhaustive search mechanism represented by a fixed window size sliding through the whole image to locate and recognize objects. This research proposes an efficient and robust Dynamic-HOG as an improvement to the traditional HOG method to locate and recognize structured objects in images. The proposed method works by locating and analyzing the structured objects in images in order to define a dynamic window size w.r.t. each object size. Moreover, the Dynamic-HOG method requires much less processing time by eliminating the exhaustive search mechanism. The method defines the height and width thresholds of objects and bounds each object with a window w.r.t. its size while ignoring non–object edges. It fits structured objects of a close range of heights and widths. This paper considers the characters that are minted on coins of different languages and sizes as the objects to recognize. There are several papers in the literature discussing coin recognition problem and proposing solutions based on various sets of features extracted from the entire coin image. This research also proposes a new method for coin recognition by focusing on recognize coins based on smaller part of the coin image which are the characters. Our method is evaluated on coins from diverse countries with different background complexity. The proposed method achieved precision and recall rates as high as 98.08 and 98.23%, respectively; which demonstrate the effectiveness and robustness of the proposed method.

Euro-mixing in Slovenia: ten years later

The paper reports the study of coin mixing after the introduction of the Euro to Slovenia in a 10-year retrospective. The national sides of the coins differ and allow recognition of coins with respect to their origin. At the beginning, after the introduction of the Euro to the country, the mixing process quickly became evident with a quick peek into a person’s purse. Even later, this peeking habit does not lose its charm. One finds coins of other countries, meets commemorative coins, and observes the frequency of coins with different origins. This pleasant activity was developed in a classroom study that allows students to become familiar with the process of coin mixing, to construct a physical model of this process, to compare this model to the collected data, and to extract information from these data. In addition, being involved in the study, students also experience characteristic problems for various measurements in social studies, where individuals with independent minds and desires are involved, problems not encountered with measuring equipment in laboratories during research in physics. In this paper we present the two theoretical models of the Euro-mixing process, ‘the initial model’ valid in the period after introduction of the Euro, and ‘the long-term’ model valid several years later, when shares of different nationalities became constant. The models are confined to a two-stage empirical study. The analysis of data has shown that the share of Slovenian Euro coins in the period after the introduction decreased with a characteristic time around 600 days. The quasi-stable share of Slovenian coins today is around 28% and is much higher than predicted by the initial model. Therefore, the long-term model is consistent with the observed behaviour of Slovenian coin shares.

Comparison of preprocessing techniques for coin recognition using image processing methods

Coin is very important role in human’s day life. For daily routine like shop, super market, banks etc the coins to be used. The coin is important part of economies and currency and it is used to pay for goods and also for our needs. Here the Indian coin has many number of count five rupee, ten rupee, two rupee, from this any one of the coin we are going to extract the texture feature for our Indian coin, first step is to preprocess the image is that method to enhance the image and remove the noise from enhanced image. For extracting clear information the image has to be preprocessed through some of the filtering techniques such as image size has to be resized, changing the contrast of the image, changing RGB to grayscale conversion for further operation such as segmentation and classification. At last the values to be compared by using PSNR, SNR, MSE of Filter noise removal with respective coin images.

Coin Recognition and Classification: A Review

Coin Recognition and Classification: A Review

English

English

Image based Coin Recognition System - A Survey

Coins have very much importance in human’s day to day life, which are used in everyone’s daily routine like banks, super markets, vending machines etc: So, there is a basic need to automate the counting and sorting of coins. Coin recognition applications play an important role in industry and computer vision. The main objective of this system is to classify high volumes of coins with high accuracy and to recognize the coins of different denominations and count the total value of the coins. Many approaches developed for the coin detection and calculate its corresponding values. For this several approaches are compared here to improve the performance of the system.

Image-based coin recognition using rotation-invariant region binary patterns based on gradient magnitudes

Most features of image-based coin recognition have been based on histogram information to achieve rotation-invariant property. However, discrimination of the features based on histogram information can be reduced by ignoring local spatial structure. In this paper, we propose a novel feature of image-based coin recognition that exploits a spatial structure. In order to consider the structure of a coin, rotation-and-flipping-robust region binary patterns (RFR) is adopted. The proposed method computes gradient magnitudes in a coin image, and extracts RFR using local difference magnitude transform to increase the accuracy of coin recognition. Comparative experiments with a number of state-of-the-art methods have been performed on the MUSCLE CIS-Benchmark Preview data set. The experimental results showed that the proposed method outperformed the state of the art methods in terms of recognition accuracy, smaller feature dimension, and shorter feature extraction time.

Develop an Efficient Algorithm to Recognize,Separate and Count Indian CoinFrom Image using MATLAB

Coins are important part of our life. We use coins in places like stores, banks, buses, trains etc. So it becomes a basic need that coins can be sorted, counted automatically. For this, there is necessary that the coins can be recognized automatically. Automated Coin Recognition System for the Indian Coins which is Rs. 1, 2, 5 and 10 with the rotation invariance. We have taken image of coin. So this system is capable to recognizing coins. Features are taken from the images using techniques as a Thresholding, canny edge detection, character recognition etc

Direct printing of reduced graphene oxide on planar or highly curved surfaces with high resolutions using electrohydrodynamics.

Electrohydrodynamic inkjet printing of reduced graphene oxide (RGO) is de-monstrated to form complex geometric devices with high resolution (line width ≈ 5 mm). Both planar and highly curved surfaces (radius of curvature ≈ 60 mm) can be used as substrates. Demonstrations of counterfeit coin recognition using RGO patterns and all-printed RGO transistors suggest substantial promise for applications in security and electronics.