An Improved Handwritten Word Recognition Rate of South Indian Kannada Words Using Better Feature Extraction Approach

Ever since the evolution of communication in human day to day activities, hand writing has gained its own impact and popularity. Therefore, Handwritten Word Recognition (HWR) is quite challenging due to heavy variations of writing style, different size and shape of the character by various writers. Accuracy and efficiency are the major parameters in the field of handwritten character recognition. However, with the progress in technology, human computer interactions have become a mandatory process to carry on the fast and dynamic demanding activities of the everyday cycle. This paper thus throws light on an effective recognition process for the handwritten word recognition. The HWR is carried out in 3 stages. In the first stage, preprocessing removes the unwanted data like noise and the second stage extracts the best features such as the sharp corners, curves and loops and finally the third stage of the process classifies the image under the correct matching class using the Euclidean distance based classifier. This process is implemented and the results indicate an improved accuracy and efficient recognition rate.