An Efficient Method for Stamps Recognition Using Histogram Moment with Haar Wavelet Sub-bands

Abstract

     One major problem facing some environments, such as insurance companies and government institutions, is when a massive amount of documents has to be processed every day. Thus, an automatic stamp recognition system is necessary. The extraction and recognition of a general stamp is not a simple task because it may have various shapes, sizes, backgrounds, patterns, and colors. Moreover, the stamp can be printed on documents with bad quality and rotation with various angles. Our proposed method presents a new approach for the preprocessing and recognition of color stamp images. It consists of four stages, which are stamp extraction, preprocessing, feature extraction, and matching. Stamp extraction is achieved to isolate complex background and remove unwanted data or noise that is surrounding the stamp area. The preprocessing stage is necessary to improve the stamp brightness and eliminate the rotation that occurs during the stamping process. In feature extraction, the extracted information will be representing the desirable feature vector in order to discriminate between stamps using local distribution of statistical features and Haar wavelet with histogram moment. Finally, each extracted feature vector will be saved in the dedicated system database for matching purpose. The test results indicate that the proposed system provides a high recognition rate for two sets of the proposed features (i.e., 99.29% recognition rate for the local distribution of statistical features and 96.01% recognition rate for the Haar wavelet transform with histogram and moment).