Ensemble Deep Learning for Brazil Currency Coin Prediction

Muppalaneni, Naresh Babu; Prathima, Ch.; Sekhar, Akula Chandra

doi:10.1088/1757-899x/1074/1/012009

Cited by 5 publications

(4 citation statements)

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“…From this classification results data, it is concluded that the newly proposed technique is giving better performance results than the technique used in Ref. [36,37,38,39]. This is because most of the existing techniques used traditional machine learning algorithms which highly feature dependents.…”

Section: G Comparative Analysismentioning

confidence: 88%

“…In the case of Brazil, a country renowned for its rich cultural history and diverse coinage, the task of classifying Brazilian coins presents unique challenges due to the substantial variations in size, design, and appearance across different denominations and minting years. Currency recognition techniques may be used in coinbased printing devices, vending machines, automated toll gates, various bank hardware, etc., [1]. According to the prediction made by the International Chamber of Commerce (ICC), there will be a global financial loss of US $2.3 trillion by 2022 due to the exchange of fake currencies [2].…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Framework for the Classification of Brazilian Coins

Swain,

Rupapara,

Nour

et al. 2023

IEEE Access

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In this quickly developing world, automatic currency identification and recognition are crucial tasks. Several financial institutions, such as banks and hardware-based devices such as vending machines and slot machines, play an essential role in all monetary unification fields. Accurate coin recognition is essential in various contexts, including vending machines, currency exchange, and archaeological research. However, the distinctive visual characteristics of Brazilian coins, including variations in size, color, and design, pose significant challenges for automated classification. Most of the existing currency recognition systems are based on the physical properties of the currencies, such as length, breadth, and mass. At the same time, imagebased methods rely on other properties like color, shape, and edge. This paper presents a novel deep-learning framework tailored to classify Brazilian coins. Our proposed deep learning framework leverages state-of-theart convolutional neural networks (CNNs) to address these challenges. We introduce a Repetitive Feature Extractor Convolution Neural Network (RFE-CNN) model to recognize the currency faster and accurately. Our framework employs a multi-stage approach for coin classification. First, a pre-processing module handles coin localization and image enhancement to mitigate variations in lighting and background. Next, an RFE-CNN-based feature extractor extracts discriminative features from the coin images. We explore transfer learning from pre-trained models to enhance the model's generalization capability, given limited data availability. We used a comprehensive dataset of Brazilian coins, comprising various denominations, minting years, and conditions, to facilitate model training and evaluation. The dataset includes high-resolution images captured under diverse lighting and environmental conditions, ensuring robust model performance in realworld scenarios. In conclusion, our proposed deep learning framework offers a powerful and efficient solution for classifying Brazilian coins. The framework's adaptability makes it a valuable tool for recognizing coins from other regions with similar visual diversity and variability challenges. The proposed model has achieved a classification accuracy of 98.34% for the classification of Brazilian coins.

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Section: G Comparative Analysismentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

A Deep Learning Framework for the Classification of Brazilian Coins

Swain,

Rupapara,

Nour

et al. 2023

IEEE Access

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“…This paper is oriented around Indian currency. The authors followed the same structure as Rahman et al [2], wherein they created a dataset that contains both Indian banknotes as well as coins of different denominations and three state of the art deep learning frameworks, namely Alexnet, Googlenet, and Vgg16, were trained, tuned and compared. The result showed that Googlenet (with 22 convolution layers) was the least accurate with an accuracy of 88%, while Vgg16 (with 16 convolution layers) was the most accurate.…”

Section: Ranjendra P and Anithaashri Tp (2020) [8]mentioning

confidence: 99%

“…Such systems are deployed to reduce the odds of human error and fast-track the repetitive process of counting and sorting a large number of coins. While traditionally, such systems make use of the mechanical and electromagnetic properties [2] of the coins to classify them, it is due to the enormous development in the field of computer vision and artificial intelligence that new systems are coming forth that utilizes robust neural networks to detect, segment and classify coins based on the images of the coins processed in real-time. Although the majority of the application stems from the sectors mentioned above yet due to the increasing reliability of coin classification systems and the increasing availability of computational resources for the average person, various studies have been done aiming to develop a system that can assist visually impaired people in identifying and classifying real currency [ [3], [4]].…”

Section: Introductionmentioning

confidence: 99%

A Novel Convolutional Neural Network for Classifying Indian Coins by Denomination

Chauhan¹,

Singh²

2021

Preprint

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Coins recognition systems have humungous applications from vending and slot machines to banking and management firms which directly translate to a high volume of research regarding the development of methods for such classification. In recent years, academic research has shifted towards a computer vision approach for sorting coins due to the advancement in the field of deep learning. However, most of the documented work utilizes what is known as ‘Transfer Learning’ in which we reuse a pre-trained model of a fixed architecture as a starting point for our training. While such an approach saves us a lot of time and effort, the generic nature of the pre-trained model can often become a bottleneck for performance on a specialized problem such as coin classification. This study develops a convolutional neural network (CNN) model from scratch and tests it against a widely-used general-purpose architecture known as Googlenet. We have shown in this study by comparing the performance of our model with that of Googlenet (documented in various previous studies) that a more straightforward and specialized architecture is more optimal than a more complex general architecture for the coin classification problem. The model developed in this study is trained and tested on 720 and 180 images of Indian coins of different denominations, respectively. The final accuracy gained by the model is 91.62% on the training data, while the accuracy is 90.55% on the validation data.

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