Recognition of Mexican banknotes via their color and texture features

García-Lamont, Farid; Cervantes, Jair; López, Asdrúbal

doi:10.1016/j.eswa.2012.02.132

Cited by 54 publications

(26 citation statements)

References 11 publications

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“…There are also methods using RGB, HSV, or features in the HSI color space [37,40,45,49,50,61,68], methods using edge-based features expressed with Canny, Prewitt, or Sobel operators [40,44,54,60], and methods using histogram information-based features such as correlation, central moments, kurtosis, mean, standard deviation, and skewness [39,43,53,59,64,65]. …”

Section: Banknote Recognitionmentioning

confidence: 99%

“…Image-based banknote recognition generally uses color images obtained in the visible light spectrum [17,18,23,32,38,39,40,42,45,49,55,60,61,63,68] and undergoes general image recognition processes, such as preprocessing, feature extraction, classification, and verification. From the existing body of literature dealing with banknote recognition, important studies were selected and presented in Table 5.…”

Section: Banknote Recognitionmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Banknote Recognition Methods by Various Sensors

Lee

Hong

Kim

et al. 2017

Sensors

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Despite a decrease in the use of currency due to the recent growth in the use of electronic financial transactions, real money transactions remain very important in the global market. While performing transactions with real money, touching and counting notes by hand, is still a common practice in daily life, various types of automated machines, such as ATMs and banknote counters, are essential for large-scale and safe transactions. This paper presents studies that have been conducted in four major areas of research (banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification) in the accurate banknote recognition field by various sensors in such automated machines, and describes the advantages and drawbacks of the methods presented in those studies. While to a limited extent some surveys have been presented in previous studies in the areas of banknote recognition or counterfeit banknote recognition, this paper is the first of its kind to review all four areas. Techniques used in each of the four areas recognize banknote information (denomination, serial number, authenticity, and physical condition) based on image or sensor data, and are actually applied to banknote processing machines across the world. This study also describes the technological challenges faced by such banknote recognition techniques and presents future directions of research to overcome them.

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Section: Banknote Recognitionmentioning

confidence: 99%

Section: Banknote Recognitionmentioning

confidence: 99%

A Survey on Banknote Recognition Methods by Various Sensors

Lee

Hong

Kim

et al. 2017

Sensors

View full text Add to dashboard Cite

show abstract

“…Color and texture are extracted from the bank notes. Using local binary model to characteristic the texture [17]. Jain in 2013 suggested a method to extract the amount of currency paper.…”

Section: Shaimaa H / Mohammed G Journal Of Al-qadisiyah For Computementioning

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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“…But the main purpose of this method is to distinguish national banknotes from different countries and does not support visually impaired limitations. In [12], Garcia-Lamont et.al introduced a recognition method for Mexican banknotes. This method uses colour and texture features.…”

Section: Related Workmentioning

confidence: 99%