The Application Of Image Characteristics In The Seal Verification

Seal plays an important role in our life as the symbol of the individual enterprise social party government and even the whole country in the law. The old manual method of seal verification is discovered with many defects. Its correction is restricted by the subject factors and many kinds of subject conditions. Also its efficiency is low. Thus, the study of the automatic seal verification has the important theory meaning and wide application.In this paper, a complete seal extraction scheme is proposed. Firstly, the RGB information of the input image is transformed to the corresponding HSI information by the simplified formula which is put forward according to the characters of the color distribution of image to extract the red seal image quickly. Secondly, the alternate threshold method is adopted to get the binary image of the seal having the good procession speed and result. Thirdly by the analysis of the reason of the loss in image, a method to renovate image is proposed. At last, the writing line and noises are removed by the open and smooth operation. The experiment result indicates a better extraction effect and procession speed.By the analysis of the common image features, a new verification method is proposed on the basis of the Pseudo-Zernike moment invariants. The Pseudo-Zernike moment invariants are extracted as features after the normalization of the seals translation and scaling. This method can be implemented without rotation compensation and is not restrained by the shape of the seal. Compare with the Hu moment invariants-based seal verification method, it makes use of the orthogonal moment invariants having less redundancies and improved recognition rate. And compared with the Zernike moment invariants-based seal verification method, it has more high order moments to describe the detail of the image and to improve the correction of the recognition.Finally, the arithmetic supposed above is simulated by matlab7.0, and the whole scheme which includes the establishment of the standard seal characteristics the preprocessing of the seal the extraction of the seal characteristics and the classification of the seal is realized based on the Pseudo-Zernike moment invariants. Through the experiment on a mass of seal images, the improved correction rate of the recognition is testified in compare with the method based on Hu moment invariants or Zernike moment invariants.