RESEARCH ON PAPER CURRENCY RECOGNITION BY NEURAL NETWORKS

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1 Proceedings of the Second nternational Conference on Machine Learning and Cybernetics, Xi", 2-5 November 2003 RESEARCH ON PAPER CURRENCY RECOGNTON BY NEURAL NETWORKS ER-w ZHANG'", BO JANG'JNG-HONG DUAN',ZHENGZHONG BA" 'Department of nformation Science, Xi" University of technology, Xi'an , China 2School of Life Science and Technology, Xi'an Jiaotong University Xi'an , China Abstract: Paper currency recognition is widely. applied in many fields such as hank system and automatic selling-goods system. How to extract high qualified monetary characteristic vectors from currency image is an impoitant problem needing to he solved now. t is a key process to select original characteristic information from currency image with noises and uneven gray. This article, aiming at the specialties of RENMNGB(RMB) currency image, puts forward a method using linear transform of image gray to diminish the influence of the background image noises in order to give prominence to edge information of the image. Then the edge characteristic information image is obtained by edge detecting using simple statistics. Finally by dividing the edge characteristic information image in the width direction into different areas, gefflng the number of the edge characteristic pixels of different areas as input vectors to neural networks"), carrying out sorting recognition by three layer BP NN, paper currency is' recognized. The proposed method of extracting input vectors has advantages of simplicity, high calculating speed, obvious characters of original image, good rohnest to different kinds of RMB. By experimental tests, recognition ratio to the,new printing style of 100 RMB, the old printing style of 50 RMB, the new printing style of 50 RMB, 20 RMB, the new printing style of 10 RMB is 95%, 99%, 99%, 92% and 98% respectively. And the results are satisfying. Keywords: neural networks, currency recognition; characteristic extracting; edge detecting 1 ntroduction The technology of currency recognition is used to research the visible and hidden currency characters, identify features all-around and dispose of the process on time. The original information has a loss because paper currency will get to he wom and blurry, even damaged by human being in circulation. Furthermore the complexity of background designs of different kinds of RMB makes it difficult for pattem recognition to work well to the currency recognition. So it is an important guarantee for improving the recognition ratio to currency paper how to extract the characteristic information from currency image with noises and select a proper pattem recognition arithmetic. Now the main methods for paper currency recognition have geometrical dimension measure['], feature recognition of image texturelz1 and NND1 etc. NN which is nonlinear math mode is used widely in the application field of currency recognition incorporating with some optimizing arithmetic such as genetic algorithm0 and simulated anneal[51 in order to optimize NN structure and improve NN recognition precision. But how to extract characteristics information effectively and decrease the influence of noises to advance NN recognition ratio need more research. This article, aiming at the specialties of RMB currency image, puts forward a new method using linear transform of image gray to diminish the influence of the background image noises in order to give prominence to edge information of the image. Then the edge characteristic information image is obtained by edge detecting using simple statistics. Finally by dividing the edge characteristic information image in the width direction into different areas, getting the number of the edge characteristic points of different areas as input vectors to NN, carrying out classifying recognition by three layer BP NN, paper currency is recognized. This method has satisfying results by experiments and computer simulation. 2 RMB Recognition System Based on BP NN 2.1 RMB Recognition System Structure Figure[611" Structure figure of recognition system is shown in Fig.l:From Fig.1 we how that two key problems need to be considered. The first is how to extract image characteristics vectors from paper currency recognition to predigest input data of NN. The second is how to select the structure and network configuration. The two problems will be analysed and discussed~$,section 2.2 and section /03/$ EEE 2193

2 ~ Proceedings of the Second nternational Conference on Machine Learning and Cybernetics; Wan, 2-5 November 2003 mage Output vector Figure 1 F4MB recognition structure based on NN 2.2 Extracting Characteristics Vectors from Paper Currency The method of extracting characteristic information as NN input vectors must meet two conditions:(l) rehieve and reappear more image characteristics information;(2) decrease the influence of noise more because the original information will probably have a loss and cause some noises in circulation. n order to diminish the disturbance of noises, preprocessing is needed to restrain the influence of noises from background patterns and stand out characteristics information such as national emblems, words, characters of images and patterns of different kinds of RMB. According to the specialties of RMB image, a method using gray linear transform is carried out for preprocessing. One dimension gray linear transform function"' is the following: mere x is the gray of input image, f(x) is the gray of output image. The number offa and f, is selected as 3 and -128 respectively by experiments to RMB in this paper. Fig.2 (a) which is a worn currency image is changed to Fig. 2(b) by gray linear transform. From the contrast between Fig. 2(a) and Fig. 2@), we can obtain two conclusions:(l) the edge characteristics information distributing of currency image Fig. 2@) can be seen more clearly and it is easy for edge detecting;(2) image by linear transform decreases the influence of noises. Gray of noises in Fig. 2(a) corresponding to a mass of white area in Fig. 2@) increases contrast to gray of edge characteristics points by linear transform than before. mage edge characteristics points by linear transform have following spe~ialties'~~:(l) the edge points have higher gray compared with other non-edge points;(2) the number of edge points has a small proportion in the number of the whole image pixels. The following inequation can'be used to explain the conclusions above: R(k)le*N (2) Where N is the pixel amount of the whole image, k is the threshold gray according to e and N, R(k) is the amount of the pixels according to the gray from the maximum to k in the image, e is the ratio of the numbers of edge characteristics points by statistics to the number of pixels for the whole image, generally e= ;. which is as the gay threshold is the lowest gray according to e in inequation(2). According to the statistical principle above, by large numbers of image analyzing, the ratio of the number. of edge characteristics points to the number of whole image pixels, which fits different RMB images, is 12%. Fig. 2(c) is the edge characteristic image extracting from Fig. 2@). Fig.3 is the edge characteristics image extracting from other kinds of RMB. A conclusion can be obtained from these figures that the edge detecting basically reflects the edge information of original images. ' (a) 20 RME3 20 RMB image after linear transform 2194

3 ~~~~ Proceedings of the Second nternational Conference on Machine Learning and Cybernetics, Xi", 2-5 November 2003 (c) 20 RMB edge characteristic image the width direction Fig. 2 RMB charaeteristic vectors extracting (d) mage after evenly dividing (c) into N areas in (a) 100 RMB edge (c) 10 RMB edge characteristic image characteristic image characteristic image Fig. 3 Other RMB characteristic vectors extracting image The edge characteristic image shown as Fig. 2(c) is evenly divided into N areas in the width direction shown as Fig. 2(d). Then a 1 * N vecto&"l as the NN input vector is obtained by having a statistics for the edge characteristic points number of every areas respectively. n the experiment N is selected as 50 and the data are nomalized. The maximum is 1 and the minimum is NN Structure Three layer BP NN with the sttucture of 50 * 30 * 5 is selected as the final shucture by repeated adjustment based on experimental data, namely 50 input nodes, 30 hidden layer nodes and 5 output nodes. The number M of input nodes is N, the number L of output nodes is 5 representing the new printing style of 100 RMB, the old printing style of 50 RMB, the new printing style of 50 RMB, 20 RMB, the new printing style of O RMB. The number K of bidden layer is pre-decided according to a [", then revised according to training times and recognition ratio of NN. Sigmoid function, as f(x) = 1 /[+ exp(-x)], is used as transfer function in hidden layer and output layer. nput vector is corresponding to the one dimensional vector which can show the distributing of currency edge characteristics points. &e dimensional vector 'is produced by calculating the different numbers of pixels which gray above threshold T, and the number is normalized from 0 to 1. Output vector is one dimensional vector, such as [ , number 1 expresses the detected paper cwency is the kind of its corresponding position, number 0 is reverse meaning. According to the output specialty of saturating and non-linearity of Sigmoid function, the output approaches to 0 or. So during NN training, adjustment come for the Sigmoid function output being close to 0 or 1 is very slow. Therefore when tlie Sigmoid function output is less than 0.01 or more than 0.09 during NN training course, the output is selected as 0 or directly. During NN test course, the data of members from output vectors can be obtained from, 0 to 1.The biggest number of 5 members must be more than 0.5 in order to make sure the kindof detected currency. To perform the network training: experiments were conducted to select the optimum network configuration. Network finial error is nertia coefficient is Learning rate of bidden.layer and output layer is 0.1. Random-number matrix and threshold matrix are initialized. The maximum and the minimum of the random number are + and order to speed up the constringency of NN 2195

4 Proceedings of the Second nternational Conference on Machine Learning and Cybernetics, Xi'an, 2-5 November 2003 learning, inertia emendation is used to adjust connection weight, that is the result of the previous adjustment amount multiplied by a certain proportion is used as a part of the adjustment amount now. The equation is as follows: AW(N) = d + v* AW(N-1) (3) Where AW(N) is the right alignment amount produced this time, and AW(N - 1) is the previous one, and d is the adjustment amount produced by the calculation error this time, and is inertia coefficient. Training times can he reduced between 20 to 30 times by this way. 3 Experiment Result and Discussion n the experiment, five kinds of RMB were used new printing style of LOO RMB, new printing style of 50 RMB, old printing style of 50 RMB, 20 RMB and new printing style of 10 RMB. The data of training set and experiment set are as follows: 8 sheets to a kmd of RMB including intact and damaged currency, new and old currency, goffered and not goffered currency as the training set; 100 sheets to 5 kinds of RMB as the experiment set, namely 20 sheets to a kind of RMB. Table 1 is experiment results. Recog- nition 2:of Table 1 Experiment results training testset As is shown in Table 1, the recognition ratio of 20 h B is not good, while the recognition ratio of other four kinds of RMB is good. The main reason is that, shown in Fig. 3, the edge characteristic information of 100 RMB, 20 RMB and 10 RMB has a similar statistical feature, that is the value of a *N vector extracted fiom 100 RMB, 20 RMB and O RMB has a very similar distributing. Compared of the input vector value in experiments, we can find that the amount of the edge characteristic pixels of 100 RMB and 10 RMB are a little more than the one of 20 RMB. Aiming at the reason mentioned above, we put forward an improvement: differently magnify the difference of these areas that obviously have different information in the 3 kinds of paper currency image. From Fig. 3, We can get 'that the main difference of 100 RMB, 20 RMB, 10 RMB lies in the middle part of images and there is little difference in the right area. So the amount of the edge characteristic expressing the number and the pattem in middle area is multiplied by a proper coefficient larger than one. The amount of the edge characteristic expressing the head portrait in &e right area is multiplied by a proper coefficient less &in one. By this way, we can enlarge the difference of the three kinds of RMB mentioned above. The test results using improvement method shown as Table 2 is better than the previous one. RMB ReMgnition ratio of haining set mgnition d o of test set Table 2 Test results after improvement --- New Old New printing pmting printing 20 style of style of style of Wh 95% 4 ' Conclusion 1Wh 99% 99% 92% New Mhg style of 10 98% t is completely feasible to use BP NN to classify w. The proposed method of extracting input vectors has advantages of simplicity, high calculating speed, obvious characters of original image, mburest to different kinds of RMB, The extracted NN input vector can show the characteristic information of different kinds of RMB image and restrain the influence of noises to recognition ratio. Satisfying recognition ratios were obtained by experiments to five different kinds of RMB. References [l] De-Fu Wang, Shi-Wen Lian. Automatic Selling TicketsMachine on Railway Station and Cash '. Recognition Technology. Computer System Application[Jl. 7:12-14, 1999 [2] ' Zhe-Xing Ymg, Zhe-Bing Qian and. Jie-Gu Li. Currency Recognition Using Mathematical Morphology and Neural Networks. JOURNAL OF 2196

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