RESEARCH ARTICLE A Novel Hierarchical Technique for Offline Handwritten Gurmukhi Character Recognition Munish Kumar · M. K. Jindal · R. K. Sharma Received: 2 August 2013 / Revised: 5 February 2014 / Accepted: 14 February 2014 / Published online: 18 November 2014 © The National Academy of Sciences, India 2014 Abstract The increasing need of a handwritten character recognition system in the Indian offices such as banks, post offices and so forth, has made it an imperative field of research. In present paper, Authors have presented a novel hierarchical technique for isolated offline handwritten Gurmukhi character recognition. A robust feature set of 105 feature elements is proposed under this work for rec- ognition of offline handwritten Gurmukhi characters using four types of topological features, namely, horizontally peak extent features, vertically peak extent features, diag- onal features, and centroid features. For classification Support Vector Machines (SVMs) classifier has been used in this work. SVMs classifier has been considered with four different kernels, namely, linear kernel, polynomial kernel, RBF kernel and sigmoid kernel. For training and testing of a classifier, we have used 3,500 samples of isolated offline handwritten Gurmukhi characters written by one hundred different writers. Maximum recognition accuracy of 91.80 % have been achieved with proposed technique, while using PCA feature set and SVM with a linear kernel classifier. Keywords Character recognition · Feature extraction · Classification · Feature selection Introduction These days, we are being influenced a lot by computers and approximately all the imperative processing is being done electronically. Keeping in mind, today’s demand, it becomes important that the transfer of data between the human being and the computer should be simple and fast. Optical Character Recognition (OCR) is an important area of research, especially for handwritten text recognition. Achievements of the commercially on hand OCR system are yet to be widened to handwritten text recognition. It is a laid down fact that frequent discrepancy in writing styles of individuals makes recognition of handwritten characters complicated. In recent times, offline handwritten Gur- mukhi character recognition has been explored by researchers owing to its practical usage. The offline handwritten character recognition system consists of the phases, namely, digitization, preprocessing, feature extraction, and classification. Digitization is the process of translating a paper based handwritten document into electronic form using a scanner. Preprocessing is used to extract meaningful information of a bitmap image. In this phase, the bitmap image is transformed into a thinned image using parallel thinning algorithm [25]. In feature extraction phase, features of a thinned bitmap image of a character are extracted. The performance of handwritten character recognition system, primarily, depends on the features that are being extracted. Authors have extracted horizontally peak extent features, vertically peak extent features, diagonal features and centroid features, in order to find the feature set for a given character. Classification M. Kumar (&) Department of Computer Science, Panjab University Rural Centre Kauni, Muktsar, Punjab, India e-mail: munishcse@gmail.com M. K. Jindal Department of Computer Science and Applications, Panjab University Regional Centre, Muktsar, Punjab, India e-mail: manishphd@rediffmail.com R. K. Sharma School of Mathematics and Computer Applications, Thapar University, Patiala, Punjab, India e-mail: rksharma@thapar.edu 123 Natl. Acad. Sci. Lett. (November–December 2014) 37(6):567–572 DOI 10.1007/s40009-014-0280-1 Author's personal copy