Handwriting forgery detection based on ink colour features

Document forgery detection is a vitally important field because the forensic role is used in many types of crimes. Adding new text is the most common type of document forgery methods because it is easy to apply and hard to detect. In this paper, a novel method is proposed to detect the forgery in a text by detecting different ink using image processing instead of conventional methods. All documents are scanned as an image and segmented into objects. Then nine features are extracted from each object based on red, green and blue channels. Distance measurements between each nearby pairs of feature vectors are computed using root mean square error. Modified Thompson Tau test is applied to extract anomaly points. The tampered points are then obtained exactly from anomaly points. Modified Thompson Tau test has a high-efficiency detection and a low omission ratio but its precision is not ideal. Therefore, the second outlier detection has been used to help to make up the difference in precision. The experimental results show that our proposed method can not only detect but also localize tampered objects efficiently.