Paper Currency Recognition research

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it is abstract of four paper in paper currency recognition made by eng.magda shawky mohamed eissa

Transcript of Paper Currency Recognition research

Paper currency recognitionPaper Currency Recognition System using Characteristics Extraction and Negatively Correlated NN Ensemble Paper currency recognition are significant in many applications. The requirements for an automatic banknote recognition system offered many researchers to build up a robust and dependable technique. Speed and precision of processing are two vital factors in such systems. Of course, the precision may be much significant than the speed. The designed system should have an important precision in detecting torn or worn banknotes. The currency recognition is one of the significant application domains of artificial neural networks. This paper discusses the ENN for currency recognition. NCL was used for the training of the network. The use of NCL is to produce the diversity among the individual networks in ensemble. The final decision of the network is taken from voting among the individual NN.

Literature ReviewPresently ,there are a number of methods for paper currency recognition: Using symmetrical masks technique for recognizing paper currency in any direction. Other method:1. The edges of patterns on a paper currency are spotted.2. In the next step, paper currency is divided into N equal parts along vertical vector.3. Then, for each edge in these parts the number of pixels is added and fed to a three-layer, back propagation neural network.4. In this process, to conquer the problem of recognizing dirty worn banknotes, the following linear function is used as a pre-processor:

f(x) = Fax + Fb (1)

where x is the given (input) image in gray scale, f(x) is the resultant image;and Fa= 3 , Fb = -128 and N =50

other method use infrared or ultraviolet spectra may be used for discriminating between genuine and counterfeits notes. Most of paper currency recognition techniques use a single multilayer feed-forward NN for the recognition. These uses edge detection technique for feature extraction. This reduces the network size. For new notes feature extraction from edge detection is simple. But for the noisy notes it is very difficult. If a network takes a false classification it will be not practical. So a single network is not reliable enough. Therefore ENN is presented in this paper to solve this problem.

Characteristics extraction Size The first phase of recognition in the algorithm considers size of the banknote. The edges of banknotes are generally worn and torn due to circulations. Hence, its size is reduced, or even is increased slightly in rejoining the torn banknotes. the size condition in the decision tree is presented as: | x x0 |< dx & | y y0 |< dy (2) Where x0 and y0 are size of the testing paper currency, and x and y are size of the reference paper currency.dx and dy are vertical and horizontal directions.

ColorImage of the banknote is transformed to an image in gray scale]. Then the gray scale level is reduced to have a significant judgment about the background color.

TextureFor recognizing the template, Markov chain concept is used in representing random phenomenon. A random process {xk, k = 0, 1, 2....} is called a Markov chain if the possibility value in state xn+1 depends on just the possible value in state xn , that is:

P(xn+1 = | xn = , xn-1 = n-1 ,..,x0 = 0 ) = P(xn+1 = |xn = )

This possibility can be shown by Pij. The state space of a Markov chain can be shown in a matrix that is:

P=

where n is the number of states in the chain.

Steps for paper currency recognition :1. Banknote Size is calculated. If its size satisfies equation (2) it is considered as a possible true banknote.2. The banknote image histogram is calculated.3. The transition matrices (Nx and Ny) are calculated then, the main diagonal elements of the matrices (namely Dx and Dy) are taken out as a feature for distinguishing between different denominations.4. The paper currency under observation is assigned to a denomination class if the Euclidean distances between the main diagonal elements of its transition matrices (Dx and Dy) and the main diagonal elements of the corresponding matrices of the reference banknote (DRx and DRy ) are smaller than a predefined value.5. At the end, the computed histogram in stage 2 is compared with the histogram of the winner class in stage 4. If the Euclidian distance between the two histograms is larger than the predefined value, the banknote is assigned to an unknown class.

An approach using negative correlation learning NCL is used for the training of the network. The use of NCL is to produce the diversity among the individual networks in ensemble.

Assume a training set S of size N. S = {(x (l), d(l), x(2), d(2)),..(x(N), d(N))} Where x is the input vector and d is the desired result. Consider approximating d by forming an ensemble whose result F(n) is the average in the component NN result Fi(n)

(5)

Where M and n refer to the number of NN in ensemble and training pattern, respectively. The error function Ei of the network i in NCL is given by the following eq (6).

(6)

(7)

Where Ei(n) is the value of the error function of the network i for the nth training pattern. The first term of (7) is the empirical risk function of the network i. In the second term, Pi is a correlation penalty function is given by eq (8).

(8)

The partial derivative of Ei(n) with respective to the output network i on the nth training pattern is

= = = (1-) (Fi(n) d(n)) + (F(n) d(n)) The NCL is a simple extension to the standard Back-propagation algorithm [8]. In fact, the only alteration that is needed is to compute an extra term of the form for the ith network. During the training process, the entire ensemble interacts with each other through their penalty terms in the error functions. Each network i minimizes not only the difference between Fi(n) and d(n) , but also the difference between F(n) & d(n). That is, negative correlation learning considers errors what all other networks have learned while training a network.

Comparative study of different paper currency and coin recognition method

Currency has great importance in day to day life so currency recognition is a great area of interest .We can conclude that image processing is the most popular effective method of currency recognition .Image processing based currency recognition technique consists of:1. Image acquisition (using cameras or scanners).2. Pre-processing (features extracting).3. Recognition of currency.

Currency can be of two types:1. coin currency.2. paper currency.

Coin currency recognition methodCoin recognition by method to designed a neural network (NN) by using a genetic algorithm(GA) and simulated annealing.(2000) Effective in a small number of input signals. Small size neural network is developed. Low cost. Accuracy is 99.68%.

Coin-o-omatic(2006) Designed to perform reliable classification of heterogeneous coin collection. Uses combination of coin photographs and sensor information in classification. Perform automatic classification of coin in :1. Segmentation.2. Feature extraction (using edge angle-distance distribution).3. Pre-selection.4. Classification (nearest-neighbor).5. Verification. Accuracy is 72%.

Image abstraction and spiral decomposition based system (2007) Obtain abstract image (considering strong edges) from the original image. Features extraction (spiral decomposition method) Spiral distribution of pixels is the key concept that enables the system to recognize the similarity between a full color multicomponent coin images. No cost in image segmentation.

Image based approach using Gabor wavelet (2009) Extract features for local texture representation. Divide image into small section (using concentric ring structure). Statistics of Gabor coefficients within each section is concatenated into a feature vector for whole image. Matching between two coin image (via Euclidean distance and nearest neighbor). Accuracy of 74.27%.Paper currency recognition

Paper currency recognition for euro using three layer perception and radial basis function (RBF) (2003) Used three layer perception for classification and RBF for validation. RBF network has a potential to reject invalid data.

Currency recognition using ensemble neural network (ENN) for TAKA (bangladesi currency) (2010) Neural network in ENN is in fact a classifier trained via negative correlation learning (NCL). The currency image converted to gray scale and then compressed, each compressed pixel is an input to the network. ENN is useful in different types of currency.

Block LBP(local binary pattern) for characteristics extraction in paper currency recognition (2010) Is improved version of LBP. Works in two phases:1. Model creating : Preparing template. Features extracting.2. The verification High recognition speed. High classification accuracy.

Side invariance paper currency recognition based on matching input note image with database of note image (2012)Overall process are: Image acquisition and segmentation. Dimension matching. Template matching. Decision making.

Recent developments in paper currency recognition system

Main steps in any currency recognition are:Matching algorithmFeature extractingCurrency note localization(edge detection and segmentationImage aquistion

Final output decision

Image acquisition: getting currency image by digital camera or scanner. Edge detection: identifying the points at which the image brightness changes sharply. Image segmentation: dividing the image into its constituent regions or object. Feature extraction: one of challenging tasks, identify the u