HOMER: Cryptography based Currency Detection System for Visually Impaired People

Jangir, Hrishikesh; Raghav, Nikunj; Kashyap, Nikhil; Tanwar, Poonam; Kumar, Brijesh

doi:10.1109/icssit48917.2020.9214204

Cited by 6 publications

(2 citation statements)

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“…In the literature, several techniques have been introduced by many researchers for recognizing images. Some of them are relied on image processing algorithms, as in [5], which used many image processing techniques for the Indian paper currency recognition model with an accuracy of 90 %. Meanwhile, other authors have relied on deep learning and machine learning algorithms using feature extraction for an image, as in [6], which used DL in image classification to classify the intestinal hemorrhage with an accuracy of 95 %.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Therefore, the problem deals with image classification, and it would be the most suitable approach in solving such a problem. Deep learning is the relevant solution, as it matches our case that is needing to classify an image of a certain set of paper currency images and to provide a new prediction based on an extracted model from the deep learning method [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Deep learning-based iraqi banknotes classification system for blind people

Awad¹,

Sharef

Salih

et al. 2022

EEJET

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Modern systems have been focusing on improving the quality of life for people. Hence, new technologies and systems are currently utilized extensively in different sectors of our societies, such as education and medicine. One of the medical applications is using computer vision technology to help blind people in their daily endeavors and reduce their frequent dependence on their close people and also create a state of independence for visually impaired people in conducting daily financial operations. Motivated by this fact, the work concentrates on assisting the visually impaired to distinguish among Iraqi banknotes. In essence, we employ computer vision in conjunction with Deep Learning algorithms to build a multiclass classification model for classifying the banknotes. This system will produce specific vocal commands that are equivalent to the categorized banknote image, and then inform the visually impaired people of the denomination of each banknote. To classify the Iraqi banknotes, it is important to know that they have two sides: the Arabic side and the English side, which is considered one of the important issues for human-computer interaction (HCI) in constructing the classification model. In this paper, we use a database, which comprises 3,961 image samples of the seven Iraqi paper currency categories. Furthermore, a nineteen layers Convolutional Neural Network (CNN) is trained using this database in order to distinguish among the denominations of the banknotes. Finally, the developed system has exhibited an accuracy of 98.6 %, which proves the feasibility of the proposed model.

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%