Feature Extraction of Currency Notes: An Approach Based on Wavelet Transform

Rajaei, Amir; Dallalzadeh, Elham; Imran, Mohammed

doi:10.1109/acct.2012.53

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…Hassanpour et al [34] used a probabilistic-based approach with Hidden Markov Model and texture-based features. Rajaei et al [35] used features from statistical moments of the coefficient matrix obtained from Discrete Wavelet Transform (DWT) of the currency images. Essentially, both approaches lack the local features, which could lower the efficacy of query images with improper views or cluttered backgrounds.…”

Section: ) Hand-crafted Feature Modelsmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: ) Hand-crafted Feature Modelsmentioning

confidence: 99%

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

et al. 2022

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“…In this method the accuracy reached 97%.In 2012,a Side Invariant Technique [9] has been applied to recognize Indian paper currency. the accuracy was reported to approach 99.5%.Moreover, In 2012, Discrete Wavelet Transform (DWT) [10] has been applied to recognize Iranian and United Arab Emirates currency notes. Also, in 2012, an algorithm in which Weighted Euclidean Distance was combined with Neural Network [11] has been applied to recognize Bahrain Paper Currency.…”

Section: Related Workmentioning

confidence: 99%

Banknote Recognition System for Visually Impaired People

Orabi

Nasr

Al-Marakeby

2020

Journal of Al-Azhar University Engineering Sector

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Paper currency recognition is one of the most important needs for visually impaired people to deal with different life circumstances. In this paper a layered framework is developed in which an Egyptian banknote recognition system is given. The developed system can recognize banknote in all geometric transformation (scaling, rotation, translation, shearing) and all lightning effects .The system is validated by using dataset which contains 1200 images. The underlying image dataset has been divided into five main categories (clear , rotated, noisy, folded and old images).The system is tested and the recognition accuracy is 93% in all categories. The first layer in the framework deals with watermark security. The second layer of the framework, the segmentation has been applied, so that its value, special textures and special drawings are extracted. In the third layer of the framework, the local banknote features have been utilized. the fourth layer is a classification layer where different techniques are applied. Moreover, the developed system recognizes multiple banknotes. The implemented framework paves the way for various mobile applications are used effectively and commercially to assist visually impaired people.

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“…Using Discrete-Wavelet-Transform (DWT) with a group of statistical measures extracted from the approximate matrix [15]. Althafiri et al in 2012 forwarded a new image technique based on "Birhani" identification based on euclidean distance based some values and neural network.…”

Section: Paper Currency Recognition: M Aoba Et Al In 2003mentioning

confidence: 99%

Paper Currency Detection based Image Processing Techniques: A review paper

Shaker

Alawan

2018

J. Al-Qadisiyah Comp. Sci. Math.

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Abstract:The currency has a great meaning in everyday life. Thus currency recognition has gained a great interest for many researchers. The researchers have suggested diverse approaches to improve currency recognition. Based on strong literature survey, image processing can be considered as the most widespread and effective technique of currency recognition. This paper introduces some close related works of paper-currency recognition. This paper has explained a variety of different currency recognition systems. The applications have used the power of computing to differentiate between different types of currencies with the appropriate layer. Choosing the proper feature would improve overall system performance. The main goal of this work is to compare previous papers and literatures through reviews these literatures and identify the advantages and disadvantage for each method in these literatures. The results were summarized in a comparison table that presented different ways of reviewing the technology used in image processing to distinguish currency papers.

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Feature Extraction of Currency Notes: An Approach Based on Wavelet Transform

Cited by 9 publications

References 4 publications

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

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

Banknote Recognition System for Visually Impaired People

Paper Currency Detection based Image Processing Techniques: A review paper

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