Research On Number Identify On Dollar Bank Note

The numbers on the paper currency is one of the important characters for it. It is not only the amount identifier of the paper currency issuance, but also for paper currency's turnover and manage has important effect, too. Each sheet has its own numbers and the same numbers haven't been used twice, so the numbers on it can identify paper currency's identity. In this paper the numbers on the paper currency and value number called "number". Developed a aptitude system by computer vision for paper currency numbers recognition, the number is recognized and recorded with binding by the system, then the organization of the paper currency could be easy. So the automatic register system has utilize tremendous and amplitude application foreground. In this paper, the automatic recognition system for paper currency numbers was built by the technique of image processing and pattern recognition.Firstly, the system samples the images of paper currency passing quickly on the CIS. Then in pre-processing stage, for the allowance of the real time demand, we directly apply linear fitting to determine the border of paper currency to achieve localization and adjustment. The image gotten by CIS has many noises. Through the experiment of average filter and median filter, the algorithm of Gauss was adopted to dispel the noises.Since the Artificial Neural Network has the property of self-organization, self-adaptation, and fault tolerance, it classifies the samples by training, the paper applies the ANN as the final classifier of samples. The system uses the modified BP network to classify the feature. The experiment shows that the algorithm in the paper is quite effective in recognition rate as well as real time property.In the number identify phase, the paper advance in level location, the gray projection method was adopted, in perpendicular orientation, an improved method of distance of traversing number body was adopted. Then every number has a notable peak, and the two or more peaks made by projection directly is avoided. The algorithm is simply and fast enough to content the processing system.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 the former, too. The method that based on BP neural network only considers two conveyed directions that are head and reverse because it adopts the technique of identify the character. Under the circumstance that 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 dollar and other countries' banknote. A new stricture-analyzing algorithm was be put forward by calculate the transfer of the number body times.Take the dollar as an example, 300 sheets of 100 par values was random chose, the recognition ratio is 98 percent.