International Journal of Software Engineering and Its Applications Vol. 10, No. 5 (2016), pp. 77-86 http://dx.doi.org/10.14257/ijseia.2016.10.5.08 ISSN: 1738-9984 IJSEIA Copyright ⓒ 2016 SERSC Offline Handwritten Gurmukhi Character Recognition: A Review Neeraj Kumar 1 and Sheifali Gupta 2 1 Electronics & Communication Department Chitkara School of Engineering and Technology Chitkara University, Himachal Pradesh 2 Electronics & Communication Department Chitkara institute of Engineering and Technology Chitkara University, Punjab Neeraj.kumar@chitkarauniversity.edu.in, Sheifali.gupta@chitkara.edu.in Abstract All over India more than 12 crore people utilize Gurumukhi script for speaking, documenting & other purposes. A considerable advancement in the work associated with the recognition of handwritten and printed Gurmukhi text has been reported in last few years. From the last few decades offline handwritten character recognition has gained a lot of interest of researchers. It is well known that each individual has some different writing style, so it is very difficult to identify or recognize the handwritten characters. Researchers have worked in this field using various scripts like Hindi, English but a very little work has been done in Gurmukhi script point of view. Based on data acquirement process a concise classification of recognition system has been discussed in this article. Various feature mining techniques & classifiers like power arc fitting ,parabola arc fitting, ,diagonal feature extraction, transition feature extraction, K-NN classifier (K- nearest neighbor) & SVM classifier (Support vector machine) are also illustrated in this paper. The methodology for word recognition has also been discussed in this paper. Keywords: Handwritten character recognition (HCR), K-NN classifier SVM classifier, feature extraction 1. Introduction Printed & HCR are the main categories of a character recognition system. In printed recognition system, a printed document is initially scanned and transformed into a machine processable form. The machine processable metaphors are pre-processed and segmented to character level to extract features from it. The term Handwritten Character Recognition is the process of conversion of text written in handwritten form into machine process able form.HCR is categorized into two parts i.e. offline and online. Online handwritten recognition of characters is very much dissimilar from offline recognition of handwritten characters as in offline handwriting recognition, the information related to strokes sis not present. Generally a character recognition system involves a number of actions such as Digitization, Pre-processing, Segmentation, Features extraction etc.Reading cheques, postcode recognition and verification of signatures are the major applications of OHCR. Many researchers have worked on the problems of recognizing the offline characters.Munish Kumar et al [1] showed that training set greatly affects the efficiency of the character recognition system.R.K Sharma et al [2] projected a scheme to recognize Gurmukhi characters which is based on transition & diagonal features with the help of K-NN classifier. To find the K-nearest neighbors, the authors calculated the Euclidian distance b/w test point and reference point. Rajiv Kumar et al [3] showed how segmentation can be done in character recognition of Gurmukhi script. In this work the