An Efficient Approach to Stamp Verification

Stamps have become one of the most important security features in big companies where huge amounts of documents need processing daily. The stamp attached to a document is used to determine the authenticity of that document so that it is necessary to identify whether a stamp is forged or genuine. However, nowadays, it is easier for the general public to forge stamps. This paper presents a practical approach for stamp verification, based on the three stages process similar to some previous work: stamp segmentation, classification stamp or non-stamp, and stamp authenticity verification. In each stage, this work tries and tests new algorithms/methods to give a new way of solving the problem in each stage. Firstly, in our approach, an unsupervised learning machine method is implemented to detect all the objects in the input image, so all the regions including stamps and text are extracted. Next, two separate models of support vector machine classification are constructed. The first one is to distinguish between stamps and other objects in a document. The second model will determine the object which was classified as stamps in the first model whether it is genuine or not. The results show that this approach can perform the stamp verification tasks effectively.