The Research On Denomination Identifying Methods Of RMB Banknote

Banknote is nowdays blood of finance. Since China founded, five series of RMB have been issued, and the fourth or the fifth is now circulating. Denomination identifying of banknote is a necessarily composed module on all financial equipments. On the basis of references about denomination identifying of banknote at abroad and home, by carefully analyzing and studying the corresponding module of the banknote detaching-machine, contraposing to RMB self-characteristics, this thesis designs two recognition methods.The key techniques of the thesis are the image pre-processing and recognition. Firstly, in order to ensure accuracy, it is necessary to pre-process original images. For example, eliminate image noise, enhance image, lacate and segment image, adjust declining image etc. In the course of adjusting declining image, this thesis puts forward a method of calculating the slope of the bottom frame line with linefit, consequently the declining angle is obtained, then we can rotate the image based on the above angle, In the process of recognition, one hand the characteristics are extracted with axis-symmetry masks according to the specialty of the RMB image, on the other hand we adequately use the abilities of parallel processing and high fault tolerance for NN, apply BP algorithm to denomination identifying of banknote. Lots of experiments indicate that the recognition method not only recognizes new and slightly polluted banknotes, but also for defected and old but still circulating banknote is strongly robust, too. After RMB passing by the system, we can get two results: (1) the denomination of RMB, (2) face or inverse of RMB.In recognition algorithm we also try the method of template matching besides neural network. Although the traditional method of two-dimensional template matching is simply achieved, the calculating quantities are much larger, it may take very long time. Under this circumstance, the author puts forward the template matching algorithm based on one-dimensional gray projection. The result of experiments indicates that the latter on the premise that the right ratio of matching is higher , the recognition speed is obviously higher than theformer,too. The method that based on neural network only considers two conveyed directions which are head and reverse because it adopts the technique of axis-symmetry masks. Under the circumstances that the recognition rates doesn't change, it has better ability of popularization than one-dimensional gray projection which need consider four directions such as head upright, head reverse, tail upright, and tail reverse when it is used in recognizing new kinds of RMB and other countries' banknote.