Cumhuriyet Üniversitesi Fen Fakültesi
Cumhuriyet University Faculty of Science
Fen Bilimleri Dergisi (CFD), Cilt:36, No: 6 Özel Sayı (2015)
Science Journal (CSJ), Vol. 36, No: 6 Special Issue (2015)
ISSN: 1300-1949
ISSN: 1300-1949
BANKNOTE DETECTION METHODS AND IDENTIFYING ITS IMPERFECTION
Elham HARIRI1,*, Mahdi HARIRI2, Mahdi AFZALI3 1Graduate 2Assistant
student. Soft Ware. School of Electrical and computer and IT Engineering, Islamic Azad University, Zanjan Branch,
Professor. Electronic. School of Electrical and computer and IT Engineering Islamic Azad University, Zanjan Branch
3Assistant
Professor. IT. School of Electrical and computer and IT Engineering Islamic Azad University, Zanjan Branch
Received: 28.04.2015; Accepted: 09.07.2015 Abstract. Less important role of banknotes was expected in transactions by advent of electronic tools in banking systems ;however, banknotes have kept their status despite all electronic facilities such as Internet banking, all types credit and ATM cards . ATM ,banknote sorting machines ,banknote accuracy detection machines , and specialized banknote detection machines for the blind were invented to facilitate working with banknotes. In this article, we review detection methods of banknote type to diagnose imperfect ones. Error percentage of these machines will decline while packaging banknotes by adding such algorithm identifying torn bills and half equality by checking serial numbers in both halves of banknotes Keywords: Imperfect banknote, image processing, Wavelet, neural network, support vector machine, histogram
1. INTRODUCTION Since humans began farming and settling, various jobs were created, and dealing began. What is the most common while dealing these days is exchange of banknotes. Despite the introduction of credit cards and electronic banking facilities, bank notes have retained their special position. ATM, sorters, accuracy detection, and bank note detection devices were invented to help the blind and others in order to work easily with bank notes. What enjoys great importance is an efficient and fast method to identify different types of banknotes as well as their accuracies. Less attention has been given to determination and identification of imperfect banknote. Various methods were introduced to identify imperfect banknotes. Generally speaking, the identification process consists of two phases: first, extracting banknote image characteristics and then classifying extracted information. In the first step, information can consist of texture, size, color, and light intensity of gray images. In feature extraction phase, different methods can be applied such as wavelet, histogram, and other methods. In information classification section, multi-layer neural networks, support vector machine, calculation of minimum Euclidean distance to reference banknotes, etc. can be used. In this article, we will introduce various methods to identify Persian banknote and method of working. In addition, a new method will be introduced to identify imperfect banknote appearance because banknote originality identification is highly regarded in daily trades. Noticeable irreparable financial damages are resulted from incorrect identification. In the second section of the article, identification methods of imperfect banknotes ate studied by image processing techniques. In the third section, a proposed solution is provided to identify banknote imperfections. Finally, conclusion and discussion are presented.
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* Corresponding author. E-mail:
[email protected] Special Issue: International Conference on Non-Linear System & Optimization in Computer & Electrical Engineering http://dergi.cumhuriyet.edu.tr/ojs/index.php/fenbilimleri ©2015 Faculty of Science, Cumhuriyet University
Banknote Detection Methods And Identifying Its Imperfection
2. THEORETICAL PRINCIPLES AND RESEARCH LITERATURE In this section, some banknote detection methods in other researches are introduced. Mixing method of image processing and pattern detection is used to extract Persian number of banknote as well as using neural network to detect the type of banknote and accuracy of detection. [3]. In this method, first, pre-processing phase is done. In this phase, morphology is used to eliminate noise around the banknote. If the banknote is placed in vertical position, it is changed into horizontal condition. Then diagonal image is changed into flat one. Next, extracting Persian numbers in upper and lower part of the banknote is done. This is done by image histogram analysis. After that, image features are extracted: such as color, saturation, value, banknote length to width ratio, number of extracted numbers, length to width ratio of detected numbers, determination of area change frequency in upper part of image, and determination of area change frequency in lower part of the first digit. Finally, multi-layer neural networks are created to detect the type of banknote. In another method, other materials are provided concerning designing a neural network-based banknote detection using texture features and image color [5]. In this method, image color and texture related features form a vector where, first, m elements are related to texture and the rest are for color are selected. While classifying banknotes, the work is done through obtaining the minimum Euclidean distance of obtained information to reference. To detect image texture, Principle Component Analysis (PCA) method is used. In this method, the volume of information declines. In this step, Wiener filter is used to eliminate image noise. PCA method shifts components to a new linear coordinate, leading to reduced information volume and increased processing speed. Fig. 1 shows a sample of PCA method to eliminate extra information and reduce information volume.
Figure 1. PCA method
General information of image is not studied. Some blocks are determined and they are the only ones being studied. In the next step, algorithm extracts color information. Finding dominant banknote color is not enough for this stage. Banknote image is fallen into some sections. Each of these sections has plaid color. Color change is gradual. The section color can be an appropriate criteria to determine the type of banknote because it is a comparative algorithm. In terms of old banknotes with pale colors, color adaptation and their closeness will be obtained. In color extraction phase, median filter is used. Finally, health detection test is met through comparing detected image dimensions and reference banknote size. Final detection is done after extracting texture and color information and vector formation as network input of perceptron neural network with hidden layer and output layer sized 150, 134, and 137. Vector feature is defined as following: 1) n=67 m=70 Color feature vector= {{texture feature vector}, {(color detection algorithm output) color feature vector}} Another method was introduced as Iranian banknote detection using wavelet transform and neural network [1]. In this method, Brass wavelet transform was used to extract image features. Wavelet transform is used as replacement for classic Fourier transform to analyze multi-dimensional data. An effective method is 913
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developed to implement this plan using Discrete Wavelet Transform (DWT) by Mallat. In fact, Mallat algorithm is known as classic plan to process two-channel encrypted signal with low band. Proposed method consists of two phases: In the first phase, image pre-processing is done, the size is reduced, and RGB image is changed into gray image. To extract details and approximation of each image, 8-level decomposition is used and extracted information is applied as neural network inputs. Image selection neural network input is impossible or too difficult because they produce a considerable number of data. Thus, feature matrixes of level 6 and 7 are used to reduce image dimension which consist of image median. Then vectors are normalized in [-1, 1]. The neural network in this method consists of 57 inputs, a hidden layer with three neurons, and four outputs. A method was introduced as new algorithm of banknote digit detection according to support vector machine [4]. In this method, serial number of banknote is used to study the banknote originality. In another method, serials are read by masking technique and neural network. Other methods extract serial number location by knowing the place. Vapink et al. used support vector as an effective tool to classify images. In this method, multi-core support vector machine was used to detect banknote serial number. After serial location finding and segmentation in banknote, each character is divided into non-shared space. Each space refers to one core. In fact, support vector machine is a core method which can solve standard learning questions with training data with labels. Support vector machine decision making function is as following: 2) h(x) = (Wv(x)) + b Where Wvarphi(x) is a map of input space to feature space of image banknote in the serial number location. In training phase, serial location, segmentation, normalization, and binary making happen. Then each banknote is divided into m*n parts. Average gray value of each part is used as a feature, applied for mixed core matrix. Another solution was raised as experimental strategy for digital currency rules and regulations [5]. Various systematic methods were introduced to determine common currency health of countries. In this method, rules are automatically formulated and Fuzzy Neural Network was used to assess currency health diagnosis. Neural network inputs are image color and texture. The output is the similarity between main frame and the given sample. The procedure is as following: Image classification is used to identify the type of banknote through identifying and extracting color and texture features and finally, classifying them in data base using image processing techniques. Features are extracted from image histogram and gray areas. Six descriptive criteria consist of first degree, central time, mean, variance, standard deviation, and tilt. They are all extracted from histogram. Five extracted texture features are entropy level, correlation, contrast, and homogeneous image. Color and texture features are used as inputs to FNN form feature vector. FNN network is trained by Bayes rules and finally, the type of banknote is identified. .
Figure 2. Fuzzy Neural Network (FNN).
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Banknote Detection Methods And Identifying Its Imperfection
Another banknote detection method is edge detection [2]. In this method, image edges of banknotes are detected and the image is divided into horizontal streamers. Then the number of related points to image edges is counted in each streamer. These data are run on a three-layer perceptron neural network to classify patterns and banknote detection. Also, RBF neural network can be applied to reject or confirm this detection. RBF neural network is able to approximate data distribution with probability distribution and it is known as one of the most frequently used networks to reject unknown data [5]. Detection is the reported disadvantage using banknote image edge. This is because, first, image information is mostly neglected and secondly, wizened banknotes rise the detection error possibility due to unwanted extra edges in image. In another method, a collection of symmetrical masks and banknote gray image were used to identify banknotes. Each mask is a rectangular frame with n*m sections. Some the sections in this mask are randomly selected and darkened. The rest of transparent ones are light. Once mask is placed on an image, only the image that is behind the transparent section is obvious. If the banknote image in non-masked back frame is dark (more than 50%), number 1 is allocated; otherwise, each mask is added together. Obtained numbers from various masks are used to detect banknote to neural network [5]. In some applications such as banknote sorter machines, a fast banknote detection method is required. In banknote sorter machines, only a few limited number of banknotes are counted and sorted. When some banknotes are placed in machine, the type of banknote needs to be clarified first. Then, the total value of banknotes is calculated according to the value of banknote. Since co-detection, sorting, and counting is time taking, this process needs a fast method. One of fast detection methods of type in such machines is to use certain points in banknote instead of using the whole image. There are usually various parts (such as corners) in banknote images, giving valuable and complete information about the banknote. Banknote detection might be completely possible by only seeing and studying these points [5]. 3. PROPOSED METHOD In this method, banknote imperfection detection is focused on rather than the type. Thus, the first step is to introduce different types of imperfections. Then a method is introduced to identify these imperfections from banknote image. Prior to introduction of different types of imperfect banknotes, the applications of this method and the necessity are studied. As stated earlier, banknote detection is done in banknote sorter devices. In addition, if the banknote has some tapes and extra part, it is removed from arrangement cycle; however, this banknote with tape and extra part might be a complete, usable banknote. Higher-than-standard torn banknotes are sometimes packed as healthy ones. The help of imperfect banknote detection system in these devices does higher quality banknote packing. Employees in bank vault will not have to spend long time to separate unhealthy taped banknotes. 3.1 Imperfect banknote Higher-than-standard torn banknote, tear in sensitive parts such as serial numbers and banknote value, and unequal halves are considered as unusable imperfect banknote and needs to be handed in to central bank. 3.2 Work procedure 3.2.1 Pre-processing First, a photo is taken from banknote image with complete white background by camera. The image size is reduced to 720*355 pixel. The operation is done on gray section of image. The first step in this phase is converting RGB image to gray image. Using filer helps remove image noises. In case of tilt image, the image is fixed by calculating deviation angle and rotating image in reverse angle direction. To this end, image extra parts are eliminated by object edge finding in image. The angle is not correctly calculated if 915
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the banknote corners disappeared. To solve this problem, the image is initially divided into two section along the length. Then an orthorhombic triangle is formed for each half and we calculate the angle. After image rotation, we will have extra parts. Prewitt filter is used to eliminate extra parts by obtaining banknote edges. This process is shown in the following figure [1].
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Figure 3. a) Correspondence orthorhombic triangle with banknote edge b) Obtaining parallel line of the banknote without corner using two points
Now, we have the flat horizontal image without noise and we can run the image processing operation in order to detect imperfections. 3.2.2
Tear determination
As we stated in pre-processing section that we produce light background image, this assumption will simplify our tear-detection work. The tear level can be estimated by deducting the mentioned banknote image from reference image and calculating the remaining size in subtraction. Nonetheless, it is observed that the difference of two images is calculated greater than real tear due to erosion. While studying histogram of healthy banknote gray image, it is clear that no pixel exists which is completely similar to background color. The tear level is simply clarified by counting the number of light pixels of banknote image because it appears in torn section of banknote. All pixels are light. Fig. 4 shows torn banknote-related histogram with more than one summit in light section. Proving the previous issue is about tear determination. The maximum tear, in standard, for a banknote is 62% for a healthy banknote. The corresponding pixel is nearly 650. The banknote is considered imperfect if the tear is greater regardless the location of tear. 3500
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Figure a) banknote histogram with more-than-standard tear b) Healthy banknote histogram Figure 4. A shows the histogram of banknote in Fig, 5.
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Figure 5. Torn banknote.
3.2.3 Inversion determination and correction To determine equality of halves, serial numbers need to be compared in two halves. It is assumed that we know the location of serial numbers in both halves. If the image is upside down, assessing serial numbers will not be possible because the coordinate of serial number locations change in this condition. Appearance investigation of Iranian banknotes shows that all banknotes, except 5000 and 10000 Rial banknotes, have the highest number of light pixels in left quarter. In fact, 5000 and 10000 Rial bills have this quality in right quarter. Thus, image inversion can be recognized by counting light and dark pixels and obtaining their ratio which is more than 60% in mentioned image quarter. If it is true, the image is rotated 180 degrees. 3.2.4 Equality determination of two halves While determining half equality, serial numbers are compared in both halves. Sometimes, the serial numbers are not equal like what is shown in Fig.6. In this condition, the banknotes seem healthy but they are invalid.
Figure 6. Banknote image with two unequal halves.
The location of serial numbers differs in various banknotes. In this article, we assume that imperfection finding stage happens after identifying banknote type. So the area is definable. Two pieces of division can 917
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be provided including upper serial and lower serial numbers with a little difference. Percentage of serial number similarity can be estimated by obtaining correlation ratio. The problem is that some numbers such as 2 and 3 enjoy great correlation in image, increasing similarity error estimation. There is possibility of error while studying correlation of the whole image due to different location of numbers. Another solution to compare serials is number fraction and part-by-part correlation investigation. In this method, although error remains in correlation estimation, the error resulted from location difference disappears. Another solution is to use perceptron neural network. Error probability declines considerably in this method. 4. DISCUSSION AND CONCLUSION According to proposed method along with banknote detection, the imperfections are evaluated and studied. What has been studied so far was recognition of banknote type and originality. Using this method in banknote sorter devices, banks and financial institutions are the main beneficiaries of this project. Adding banknote imperfection detection to sorter devices will add the capability of more accurate banknote separation. 5. REFERENCES 1. F.Poor Ahangarian;T.Mohammadpuor,A.Kianisarkaleh.Persian Banknote Recognition Using Waveletand Neural Network.2012 International Conference on Computer Science and Electronic Engineering,pp.679-684. 2. Gunaranta,D.,Kodikara,N,,and Premaratne,H.,2008.ANN based currency recognition System using compressed gray scale and application for Srilankan currency note SLCRec, Word academy of science, engineering and technology ,Vol.35.pp.2070-374. 3. Salar pour, Amir; Behmanesh, Ali Asghar; Khotan Lou, Hassan. Mixed method to detect Persian banknotes, computer engineering faculty, Bou Ali Sina University, Hamedan, Iran, 2011. 4. Shan Gai ; Guowei Yang. New Banknote Number Recognition Algorithm Based on Support Vector Machine. 2013 Second IAPR Asian Conference on Pattern Recognition,pp.176-180. 5. Mahdavi, Mehregan; Ahaki Lakeh, Habib; Naser Sharif, Babak. Designing a system based on neural network-based banknote detection using image texture and color features. Faculty of computer engineering, Gilan University, Iran, 2010. 6. Wei Q.Yan;Jarrett Chambers.An Empricial Approach For Digital Currency Forensic 2013 IEEE,pp.2988-299.
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