IJSRST162347 | Received: 24 May 2016 | Accepted: 29 May 2016 | May-June 2016 [(2)3: 119-126] © 2016 IJSRST | Volume 2 | Issue 3 | Print ISSN: 2395-6011 | Online ISSN: 2395-602X Themed Section: Science and Technology 119 Implementation of Feed-forward Neural Network Models for Pattern Classification Using Transformation Based Feature Extraction Methods Sandeep Kumar, Amit Rawat Faculty of Engineering & Technology, Agra College. Agra, Uttar Pradesh, India ABSTRACT Automatic recognition of handwritten Hindi characters is a difficult and one of the most interesting research areas of pattern recognition field. A lot of work has been done in this area till date; still it is a subject of active research. Hindi characters are cursive in nature and thus characters may be written in various cursive ways. Characters also show a lot of similar features such as header line, vertical bar, curves and etc. Handwritten characters may be of varying sizes, width and orientation, which makes the problem more complicated and difficult to solve. The performance of an optical character recognition system extremely depends on the procedure used to extract quality features from characters. A number of feature extraction, classification and recognition techniques have been used successfully in this area. Proposed work is focused on some of the existing techniques like neural networks for the recognition of handwritten Hindi characters. Neural networks are good at recognizing handwritten characters as these networks are insensitive to the missing data. In this paper, we are implementing and analyzing the performance of feed-forward neural network models to perform pattern classification for handwritten Hindi characters using different transformation based feature extraction methods. Keywords: Feed-Forward Neural Network, Radial Basis Network, Discrete Wavelet Transform, Radon Transform, Pattern Recognition. I. INTRODUCTION Handwritten character recognition is wide and one of the most explored areas of pattern recognition field. It covers all sorts of character recognition in various application domains such as signature recognition, script recognition, text recognition, handwriting recognition and document analysis etc. [1]. An automatic character recognition systems can be characterized as online or offline. An online recognition system utilizes the digitizer which captures writing in the form of real time data, i.e. temporal information is available for the system. Two dimensional coordinates of the successive points in the writing are represented as a function of time. An offline recognition system works on previously written data on paper. This data is scanned by using a digital camera or scanner and presented to the system in the form of images. Scanned character images are then converted to bit pattern to be processed by the recognition system. Online recognition systems proved to be better than offline systems as they are provided information about order of the strokes, speed of the writing, pen-up and pen-down etc. as well as temporal information [2]. Due to the increasing popularity and demand, offline handwritten character recognition problem is gaining a lot of attention in last few decades. Offline recognizers are being used in banking operations such as signature verification, document analysis and processing, postal address processing, digital libraries and so on. Despite of a number of sincere efforts done so far in the area, a lot of work is left to be done and still there is some space in the state of the art. Usually, handwritten characters are non-uniform in nature, i.e. a particular character can be written in different styles by different writers or by the same writer at different time points. Due to varying writing styles, characters may not have smooth curves or perfectly straight lines all the time [3]. Thus, an offline handwritten character recognition system should be able to efficiently recognize characters with varying size,