Multiple kinds of paper currency recognition using neural network and application for Euro currency

Takeda, Fumiaki; Nishikage, Toshihiro

doi:10.1109/ijcnn.2000.857888

Cited by 41 publications

(22 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The major motivation for selecting SVM as a classifier is its suitability for binary classification problems, as in our case, we need to classify a banknote as genuine or counterfeit. Unlike other techniques such as [21,22], the proposed method does not require any special hardware and works simply with the android-based smartphone. To assess the performance of the proposed system, a test dataset of 50 images including 33 genuine and 17 counterfeit banknotes of 3 different denominations was used.…”

Section: Paper Currency Verificationmentioning

confidence: 99%

Android-Based Verification System for Banknotes

Rahman¹,

Sargano

Bajwa

2017

J. Imaging

View full text Add to dashboard Cite

With the advancement in imaging technologies for scanning and printing, production of counterfeit banknotes has become cheaper, easier, and more common. The proliferation of counterfeit banknotes causes loss to banks, traders, and individuals involved in financial transactions. Hence, it is inevitably needed that efficient and reliable techniques for detection of counterfeit banknotes should be developed. With the availability of powerful smartphones, it has become possible to perform complex computations and image processing related tasks on these phones. In addition to this, smartphone users have increased greatly and numbers continue to increase. This is a great motivating factor for researchers and developers to propose innovative mobile-based solutions. In this study, a novel technique for verification of Pakistani banknotes is developed, targeting smartphones with android platform. The proposed technique is based on statistical features, and surface roughness of a banknote, representing different properties of the banknote, such as paper material, printing ink, paper quality, and surface roughness. The selection of these features is motivated by the X-ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) analysis of genuine and counterfeit banknotes. In this regard, two important areas of the banknote, i.e., serial number and flag portions were considered since these portions showed the maximum difference between genuine and counterfeit banknote. The analysis confirmed that genuine and counterfeit banknotes are very different in terms of the printing process, the ingredients used in preparation of banknotes, and the quality of the paper. After extracting the discriminative set of features, support vector machine is used for classification. The experimental results confirm the high accuracy of the proposed technique.

show abstract

Section: Paper Currency Verificationmentioning

confidence: 99%

Android-Based Verification System for Banknotes

Rahman¹,

Sargano

Bajwa

2017

J. Imaging

View full text Add to dashboard Cite

show abstract

“…기술을 이용하여 다 권 종의 지폐를 인식하는 방향 [2] 과 지폐 특성 벡터를 추출하여 고속으로 인식하는 방 향 3) 위와 같은 과정에서 구한 좌표 데이터로부터 아 래 식 (1)을 사용하여 기울기를 구한다.…”

Section: 지폐 인식 기술의 발전은 신경망(Neural Network)unclassified

An Implementation of Pattern Recognition Algorithm for Fast Paper Currency Counting

Kim¹,

Kang²

2014

The Journal of Korea Information and Communications Society

View full text Add to dashboard Cite

In this paper, we suggest an efficient image processing method for fast paper currency counting with pattern recognition. The patterns are consisted of feature data in each note object extracted from full reflection image of notes and a general contact image sensor(CIS) is used to aggregate the feature images. The proposed pattern recognition algorithm can endure image variation when the paper currency is scanned because it is not sensitive to changes of image resulting in successful note recognition. We tested 100 notes per denomination and currency of several countries including Korea, U.S., China, EU, Britain and Turkey. To ensure the reliability of the result, we tested a total of 10 times per each direction of notes. We can conclude that this algorithm will be applicable to commercial product because of its successful recognition rates. The 100% recognition rates are obtained in almost cases with exceptional case of 99.9% in Euro and 99.8% in Turkish Lira.

show abstract

“…Kosaka and Omatu proposed the learning vector quantization (LVQ) method to recognize 8 kinds of Italian Liras [16, 17]. Most banknote recognition methods employed neural network techniques for classification [13, 16-18, 31-33]. …”

Section: Related Workmentioning

confidence: 99%

“…For instance, the average recognition rate of the algorithm [27] based on SIFT is 76%. Some neural network based banknote recognition systems achieved recognition rate no large than 95% [16, 17, 31-33]. Because of specific design, the testing images in these references were captured in highly constrained environments, such as a transaction machine or a specific sensor system which are not portable [5, 31, 33].…”

Section: Experiments and Analysismentioning

confidence: 99%

“…Recent advances in theories, sensors, and embedded computing hold the promise to enable computer vision technique to address their needs. Although a number of studies on camera-based bill recognition have been published in literatures [3, 9, 10, 13, 16-18, 31-33], some of them are restricted to specific and standard environment. For example, the whole bill must be visible without occlusion, wrinkles, etc.…”

Section: Iintroductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust and Effective Component-Based Banknote Recognition for the Blind

Hasanuzzaman

Yang

Tian

2012

IEEE Trans. Syst., Man, Cybern. C

103

View full text Add to dashboard Cite

We develop a novel camera-based computer vision technology to automatically recognize banknotes for assisting visually impaired people. Our banknote recognition system is robust and effective with the following features: 1) high accuracy: high true recognition rate and low false recognition rate, 2) robustness: handles a variety of currency designs and bills in various conditions, 3) high efficiency: recognizes banknotes quickly, and 4) ease of use: helps blind users to aim the target for image capture. To make the system robust to a variety of conditions including occlusion, rotation, scaling, cluttered background, illumination change, viewpoint variation, and worn or wrinkled bills, we propose a component-based framework by using Speeded Up Robust Features (SURF). Furthermore, we employ the spatial relationship of matched SURF features to detect if there is a bill in the camera view. This process largely alleviates false recognition and can guide the user to correctly aim at the bill to be recognized. The robustness and generalizability of the proposed system is evaluated on a dataset including both positive images (with U.S. banknotes) and negative images (no U.S. banknotes) collected under a variety of conditions. The proposed algorithm, achieves 100% true recognition rate and 0% false recognition rate. Our banknote recognition system is also tested by blind users.

show abstract

Multiple kinds of paper currency recognition using neural network and application for Euro currency

Cited by 41 publications

References 5 publications

Android-Based Verification System for Banknotes

Android-Based Verification System for Banknotes

An Implementation of Pattern Recognition Algorithm for Fast Paper Currency Counting

Robust and Effective Component-Based Banknote Recognition for the Blind

Contact Info

Product

Resources

About