International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-3, Issue-2, December 2013 419 Punjabi Offline Signature Verification System Using Neural Network Rimpi Suman, Dinesh Kumar Abstract- The signature identification or verification , means where "identification" implies matching a user signature against a signature associated with the identity that the user claim. Biometrics can be classified into two types Behavioral (signature verification, keystroke dynamics, etc.) and Physiological (iris characteristics, fingerprint, etc.).Signature and Finger Print verifications are most widely used personal verifications and are one of the first few biometrics used even before computers. Signature verification is widely studied and discussed using two approaches. On-line approach and offline approach. Online signature verification represents the dynamic information related to signature which is captured at the time when signature made. The offline signature verification represents the static information of signature. Offline systems are more applicable and easy to use in comparison with on- line systems in many parts of the world however it is considered more difficult than on-line verification due to the lack of dynamic information. This paper presents about offline Signature identification method that had more attraction in recent years because of its necessity for use in daily life routines and when the signature needs to be immediately verified like bank checks, Security for Commercial Transactions, Cheque Authentication, attendance etc. In this paper we present, features types and recent methods used for features extraction in offline signature verification systems .Finally, we suggest new interesting ideas to be incorporated in the future. General Terms Signature verification, Signature matching, biometric Keywords- Signature verification techniques ,Preprocessing ,feature extraction, feature detection, security. I. INTRODUCTION Biometric identification methods such as Signature Verification, fingerprint, face recognition, iris scanning, signature and DNA analysis are increasing because of their unique features. Today Human being Identifications are most necessary in our day to day life activities such as crossing international borders and entering any secure locations, Traditional bank checks, Biometric verification helps us to recognize people based on their extracted physical or behavioral features. These features must have some properties such as uniqueness, permanence, acceptability, collectability, scalability, portability and the cost to implement any biometric system. Basically, there are two common biometric feature Categories: 1) Physical features: This type of Biometric include face, fingerprint, brighten, ear, palm print, retina, hand, finger geometry and DNA. Most of these features are relatively static. Manuscript received December, 2013. Rimpi Suman, Dinesh Kumar 2) Behavioral features: This type of biometric includes features that measure the action of the person such as speaking, motion of body and writing. These features are not static because it changes over time due to age effect and other developmental and enhancement factors. A Signature gradually appears as person name which is written by an individual in their own handwriting. 1.1 Signature Identification: The signature is a biometric approach to identify a human being. It can be classified as online and offline signature Identification. The online Signature Identification deals with extraction the features of signature such as velocity, acceleration and pen pressure, as functions of time. these features are captured during acquisition of signature by using a device like tablet. The online signature verification system are more expensive as compared to offline signature verification system. bank credits, credit cards and various legal documents and besides the many other applications. At this point, we must require higher security levels with easier user interaction or user friendly which can be achieved using biometric verification or Signature identification. On the other hand, the offline Signature Verification system deals with the static features of signature such as area of signature image, centroids , histogram and many other features. In this paper we deal with offline signature verification using Neural Network. This research paper basically deals with the Punjabi language offline signature verification. II. THE OFFLINE SIGNATURE IDENTIFICATION: Approach: The state of the art in offline signature Identification is follows a pattern that is similar to image processing with five steps as shown in figure1. The input Signature are preprocessed, and then personal features are extracted and stored into the knowledge base, In the classification phase, personal features extracted from an inputted signature are compared with template signature stored in the knowledge base, to check authenticity of the test signature.