**1. Introduction**

Handwritten autograph plays an important role in modern life as it is routinely used in every sphere of human activity. Couto [1] utilizes a lexical similarity technique for each entity identified. This frequently makes it unattainable to differentiate between a forged signature and a signature created under influence. Chung [2] applied Fuzzy groups to handle uncertainty. Although there are contributing studies in this area, research often failed to take into account the influence of contributing factors such as distractions and singers'stress which may affect the signatures being signed [3, 4]. It is widely used for authenticating financial and business transactions [5, 6]. There are online and offline authentication systems. In contrast, online signature systems require special hardware such as pressure tablets. These devices extract dynamic information including pressure, signer's speed, and the static image of signature. Unfortunately, both online and offline signatures can easily be imitated or forged, leading to false representation or fraud [7]. Yang [8] used learned dictionary to check samples. This method has been successfully

utilized in image recognition lately. According to Alattas [9], financial institutions are interested to benefit from the reliability and safety of offline signaturerecognition systems. Another major reason is that online authentication systems require more complex processing and high-tech gadgets than off-line systems. Offline autographs are usually presented on a piece of paper, which is the norm in documentation. Currently, there is a need for efficient online and offline systems to ascertain the genuineness of personal autographs. Authentication of handwritten autographs usually consists of a series of procedures. These processes are preprocessing (where images are enhanced, binarized, divided into fragments and other related operations), feature extraction (features of the signatures are extracted as raw forms), feature selection or reduction (extracted features are reduced for efficiency), identification and authentication of the signatures against the signature database based on the selected features. A good verification outcome can be performed by likening the strong features of the taster against the autograph of a signer sample utilizing suitable techniques or classifiers [10]. Methods depend on local tests, which concentrate on the analysis of the essential features of different scripts [10–12]. Some studies utilized evolving curves which do not move away to near by features decreasing the superfluous fragmentation [13]. Based on the available gap in the literature, in this paper, we propose a new process to identify and authenticate Offline-Arabic signatures. This method uses a combination of techniques including adaptive window positioning procedure for autograph attribute extraction and feature selection method for reduced features and selection of important features. In this paper, enhanced Discrete Cosine Transform (DCT) and, Spars Principal Component Analysis (SPCA) method is used to extract attributes. Further, these extracted features are reduced to the best features only. In this research, in order to classify genuine and forged signature two types of classifiers: 1) Decision Tree and 2) Support Vector Machine (SVM) are applied. The classification outcomes of Decision Tree and SVM are compared to choose a better classifier. .
