Currency recognition system using image processing

Abburu, Vedasamhitha; Gupta, Saumya; Rimitha, S.R.; Mulimani, Manjunath; Koolagudi, Shashidhar G.

doi:10.1109/ic3.2017.8284300

Cited by 39 publications

(6 citation statements)

References 2 publications

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“…Then the latent image, segmented via template matching is encoded using HOG descriptor and classified with an SVM model to predict if the note is fake or real Singh, Ozarde & Abhiram (2018). Abburu et al (2017) proposed a system for automated currency recognition using image processing techniques for accurately identifying both the country of origin and the denomination of a given banknote. However, they do not discriminate between a fake and a real currency note.…”

Section: Related Workmentioning

confidence: 99%

DeepMoney: counterfeit money detection using generative adversarial networks

Syed

Jan

Alkhodre

et al. 2019

PeerJ Computer Science

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Conventional paper currency and modern electronic currency are two important modes of transactions. In several parts of the world, conventional methodology has clear precedence over its electronic counterpart. However, the identification of forged currency paper notes is now becoming an increasingly crucial problem because of the new and improved tactics employed by counterfeiters. In this paper, a machine assisted system-dubbed DeepMoney-is proposed which has been developed to discriminate fake notes from genuine ones. For this purpose, state-of-the-art models of machine learning called Generative Adversarial Networks (GANs) are employed. GANs use unsupervised learning to train a model that can then be used to perform supervised predictions. This flexibility provides the best of both worlds by allowing unlabelled data to be trained on whilst still making concrete predictions. This technique was applied to Pakistani banknotes. State-of-the-art image processing and feature recognition techniques were used to design the overall approach of a valid input. Augmented samples of images were used in the experiments which show that a high-precision machine can be developed to recognize genuine paper money. An accuracy of 80% has been achieved. The code is available as an open source to allow others to reproduce and build upon the efforts already made.

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Section: Related Workmentioning

confidence: 99%

DeepMoney: counterfeit money detection using generative adversarial networks

Syed

Jan

Alkhodre

et al. 2019

PeerJ Computer Science

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“…Currency recognition is one of the targeted applications in deep learning. However, there are several types of research were conducted on paper currency using conventional methods [4], [7]- [9]. Recently, numerous researchers such as [10] commenced using deep learning techniques for paper currency recognition due to the need for recognizing the true and fake paper money.…”

Section: Recent Researchmentioning

confidence: 99%

Transfer Learning Utilization for Banknote Recognition: a Comparative Study Based on Bosnian Currency

Almisreb

Saleh

2019

scjournal

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Transfer learning introduces the ability to perform deep learning models over a small set of data. This paper investigates the utilization of fine-tuned Convolutional Neural Networks (CNNs), namely, Alexnet, Googlenet, and Vgg16. Alexnet and Googlenet consider as the state 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 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 efficient performance by achieving 88.65%.

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“…[ 21 , 22 ]. As a result, the image processing technique has proven beneficial due to its better accuracy, speed, and less computational power requirements as reported in security systems [ 23 ], automobiles [ 24 , 25 ], automated parking lots [ 26 ], currency recognition [ 27 ], blood sample identification [ 28 ] and neural network-based hyperspectral image classification [ 29 ].…”

Section: Introductionmentioning

confidence: 99%