Study On Seal Identification

With the development of the computer techniques, difficulties of forging seal decline. Therefore economic crimes caused by forgery seals are hard to be forbidden. The purpose of seal identification study is to provide a system that can identify tested seal images to be genuine or forgery, and it also can offer an automatic, efficient and accurate documentation authority identification way to avoid crimes.This paper focuses on Seal identification. First, discuss the emphasis and difficulties of seal identification, Based on these emphasis and difficulties, a new registration method and four complementary features are used. This new registration method fit all kinds of seal. It first get the center of mass, then use the center of mass as center, make the radius and concentric circle discretization and resample the points in concentric circle, At last get the angle's value using small region matching and finish the registration. This method is proved efficient by experiment.According to the feature of seal, this paper brings up four different and complementary features and their different classification methods. These features are shape based on Hu Moment Invariants, difference based on seal registration, texture based on wavelet and ISIFT. Shape features are used to classify the seal of different shape by abstracting Hu Moment. We define a difference function based on seal registration. According the difference function, we choose a threshold value to realize seal identification. Texture features based on wavelet choose mean and variance of all sub-channels as an input into SVM. SIFT is a stable and powerfully matching method. Because SIFT costs so much time, this paper gives an improved SIFT method to decrease the time cost. ISIFT divides the seal into several regions after registration and edge extracting. The small region makes searching cost much less time. In addition, the points on edge are key factor to determine seal identification, we can ignore other points. Then use Euclidean distance to judge feature points after registration and edge extracting. The experiment tells ISIFT is better than SIFT in aspect of rate of registration and real-time.