| Seal plays an essential role in identifying entity status. Particularly, as a usual method, it proves law effect of financial notes stamped with seal. In most cases, the recognition of seal is completed with folding seal in diagonal and aligning manually. The accuracy of recognition result depends on man-made decision and grift crimes always happen with counterfeit financial seal, which are the reasons we manage to reaserch another unique method to solve this problem. Hence, A seal verification system is studied in this paper, based on embedded system technology. The recognition method for circle seal of finance notes is discovered from the view of system, and a series of recognition algorithms from image adjustment, preprocess, feature extraction to estimation are proposed. Based on high-speed DSP, the embedded image acquiring and processing system is designed and fabricated. Experimentally, the recognition algorithms is carried out.The followings are the main research contents:1. Image aberration adjustment. The factors leading to aberration are analyzed, and a model of aberration and reconstruction is established and is used for reconstruction adjustment of aberrated image. A section two points image adjustment method is proposed to solve the nonuniform aberration resulting from luminance distribution of LED light source, combining with the output character of CMOS image sensor. As far as geometric aberration is concerned, mesh template image obtained with polynomial fitting is used for geometric aberration parameter determination of system and then the parameters are used in geometric adjustment and gray interpolation for image reconstruction.2. Research and implement of ROI (Region of Interesting) extraction algorithm of circle seal. The red seal is selected as research sample. This seal is processed by color information of note image. For example, the color image is transferred to gray image to bring red component of original image into prominence firstly. Then a special pixel of pentagram is obtained with flatness convolution and defined as A. With the pixel A as center, multiple searches having direction to A are carried out until eight pixels in seal frame are obtained, these pixels are defined as Bi. Among of them, the pixels having smaller standard deviation will be kept while the others ignored. The circle center and radius of seal are calculated with three point determination method of circle, and then the ROI is extracted.3. Reaserch and caculation of measurement of seal orientation deflexion. Through the projection of seal ROI from its center to outer frame, a group of one dimension feature data is obtained. The correlation is calculated, between SS (Sample Seal and Model Seal) under different orientations. And then, we can determine the orientation with maximize the correlation. The experiment results demonstrate that the precision is≤0 .5°4. Authenticity recognition arithmetic of circle seal. The edge features of seal are adopted as recognition features, on basis of position and orientation precise registration of SS and MS. The corresponding relation, between edges of SS and MS, is determined and then similarity measure is calculated with improved Hausdorff distance. In order to alleviate the effect of seal quality on edge similarity, the corresponding relation is used as the measure of seal quality and artificial neural network is adopted in analyzing and estimation. Experiment shows that the recognition probability is up to 95%.5. Experiments and evaluation of recognition algorithm in DSP embeded system. This image acquirement and process system based on high speed DSP is designed and fabricated. Then the function measurement and performance of algorithm are carried out, which validate the system effectivity. The time consuming of each section of algorithm is analyzed, which provides the cornerstone for further optimum and improvement.
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