Sunil Kumar et. al. / (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 07, 2010, 2421-2426 Use of Splines in Handwritten Character Recognition Sunil Kumar Research Scholar, Singhania University Rajasthan, India Gopinath S ABB Global Industries, Banglore, India Satish Kumar Electronics and Communication NCR College, Bahadurgarh, India Rajesh Chhikara Electronics and Communication MIT Mundka, Delhi, India Abstract— Handwritten Character Recognition is software used to identify the handwritten characters and receive and interpret intelligible handwritten input from sources such as manuscript documents. The recent past several years has seen the development of many systems which are able to simulate the human brain actions. Among the many, the neural networks and the artificial intelligence are the most two important paradigms used. In this paper we propose a new algorithm for recognition of handwritten texts based on the spline function and neural network is proposed. In this approach the converse order of the handwritten character structure task is used to recognize the character. The spline function and the steepest descent methods are applied on the optimal notes to interpolate and approximate character shape. The sampled data of the handwritten text are used to obtain these optimal notes. Each character model is constructed by training the sequence of optimal notes using the neural network. Lastly the unknown input character is compared by all characters models to get the similitude scores. Index Terms—Artificial Neural Network, Back propagation algorithm, Optimal knots, Splines. I. INTRODUCTION Handwriting recognition is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices. The image of the written text may be sensed "off line" from a piece of paper by optical scanning (optical character recognition) or intelligent word recognition. Alternatively, the movements of the pen tip may be sensed "on line", for example by a pen-based computer screen surface. There has been a lot of research on handwritten character recognition in recent years, resulting in a number of proposed pattern recognition techniques. One such method uses Mahalanobis generalized distance of a feature vector calculated from a character image [6, 4]. A pattern recognition approach using statistical processing based on Bayes’ theorem was proposed by Graham for distinguishing spam (junk) mail [3, 2]. Handwritten character recognition method using the Bayesian filter algorithm was proposed by [8]. Techniques using a machine-learning approach such as a neural network (NN) and a support vector machine (SVM) are also well known [1, 5, 7]. Good recognition rates are achieved for character or numeral recognition, where the number of classes is rather small. But as the number of classes increases, as for example in isolated word recognition, the recognition rates drop significantly. An even more difficult task is the recognition of general handwritten text lines or sentences. Here, the lexicon usually contains a huge amount of word classes and the correct number of words in the image is unknown in advance, which leads to additional errors. In this field, recognition rates between 50% and 80% are reported in literature, depending on the experimental setup [9, 10, 11, 12, 13]. A novel approach of on-line handwritten character recognition using natural spline is discussed in [14]. The various methods for character recognition have already been published. But the method presented here is advanced than those methods since cursive handwriting can be recognized with the help of a combination of spline function and artificial neural networks, this becomes the primary advantage of the method over other existing methods. The purpose of our proposed method is to recognize characters using spline function. The continuous image of the character acquired is converted into discrete image using Digital Image Processing Techniques such as thinning, image filtering, rotate and converting a colored image into black white. The Spline curves of all the characters were obtained together with their error function. The Spline matrices obtained were then used as inputs to the Artificial Neural Network (ANN). And the outputs of the network were character matrices. ANN was then trained using Multilayer Back propagation algorithm, to correspond various spline curve to their respective characters. Hence the character can be recognized. The flowchart in “fig.1” shows the various steps used in this method. ISSN : 0975-3397 2421