(IJARAI) International Journal of Advanced Research in Artificial Intelligence, Vol. 2, No. 1, 2013 33 | Page www.ijarai.thesai.org An interactive Tool for Writer Identification based on Offline Text Dependent Approach Saranya K Research Scholar PSGR Krishnammal College for Women Coimbatore, India Vijaya MS Associate Professor GR Govindarajulu School Of Applied Computer Technology Coimbatore, India Abstract — Writer identification is the process of identifying the writer of the document based on their handwriting. The growth of computational engineering, artificial intelligence and pattern recognition fields owes greatly to one of the highly challenged problem of handwriting identification. This paper proposes the computational intelligence technique to develop discriminative model for writer identification based on handwritten documents. Scanned images of handwritten documents are segmented into words and these words are further segmented into characters for word level and character level writer identification. A set of features are extracted from the segmented words and characters. Feature vectors are trained using support vector machine and obtained 94.27% accuracy for word level, 90.10% for character level. An interactive tool has been developed based on the word level writer identification model. Keywords- Feature Extraction; Support Vector Machine; Training, Writer Identification. I. INTRODUCTION The significance and scope of writer identification is becoming more prominent in these days. Identification of a writer is highly essential in areas like forensic expert decision- making systems, biometric authentication in information and network security, digital rights administration, document analysis systems and also as a strong tool for physiological identification purposes. In forensic science writer identification is used to authenticate documents such as records, diaries, wills, signatures and also in criminal justice. The digital rights administration system is used to protect the copyrights of electronic media. Two broad categories of biometric modalities are: physiological biometrics that perform person identification based on measuring a physical property of the human body (e.g. fingerprint, face, iris, retinal, hand geometry) and behavioral biometrics that use individual traits of a person’s behavior for identification (e.g. voice, gait, signature, handwriting). Hence writer identification falls under the category of behavioral biometrics. Handwritten document analysis is applied in fields of information retrieval either textually or graphically [1]. Writer identification mode can be generally classified into two types as online and offline. In online, the writing behavior is directly captured from the writer and converted to a sequence of signals using a transducer device but in offline the handwritten text is used for identification in the form of scanned images. Off-line writer identification is extensively considered as more challenging than on-line because it contains more information about the writing style of a person, such as pressure, speed, angle which is not available in the off-line mode. Writer identification approaches can be categorized into two types: text-dependent and text-independent methods. In text-dependent methods, a writer has to write the identical text to perform identification but in text independent methods any text may be used to establish the identity of writer [2]. Various approaches and techniques have been proposed so far for writer identification. Writer identification using connected component contours codebook and its probability density function was proposed in [3]. This paper exhibits better identification rates by combining connected-component contours with an independent edge-based orientation and curvature PDF. In [4], eleven macro-features and micro- features have been used for writer identification. Authors in [8] have used a set of features extracted from lines of text correspond to visible characteristics of the writing such as width, slant, height of the three main writing zones and also features based on the fractal behavior of the writing for writer identification. A system for writer identification using textural features derived from the gray-level co-occurrence matrix and Gabor filters has been described in [12]. In the research work [14], Morphological features obtained from transforming the projection of the thinned writing have been computed and used for writer identification. A HMM based approach for writer identification and verification built an individual recognizer for each writer and train it with text lines of writer was proposed in [15]. A system developed in [16] for writer identification and verification takes two pages of handwritten text as input and determines whether the same writer has written those two pages and features like character height, stroke width, writing slant and skew, frequency of loops and blobs have been used. This research proposes text dependent writer identification based on scanned images of English handwriting. The scanned images are segmented into words and these words are further segmented into characters on which pre-processing and features extraction tasks are performed. Features like edge based features, word measurements, moment invariants used in the existing research work are taken