Analysis of Performance of KNN Classifier in Recognition of Handwritten Digits
B.E., Final Year, Department of Computer Science and Engineering. 1Panimalar Institute of Technology, Chennai, India, India
ABSTRACT
The recognition of digits and manuscripts is in growing need for using in different situations like, recognizing the handwritten postal address digits , to automatically redirect the letters in the mail and to acknowledge the nominal values in the bank cheques. The handwritten digit recognition often faces huge difficulty when it deals with intra-class variation because of many styles of writing, different inclination angles of the characters. Optical Character Recognition (OCR) is a technique that is a widespread functionality in mobile devices and scanners among others. It is used to identify and recognize the printed characters with the help of images. This paper explains the use of the KNN (K Nearest Neighbor) algorithm used in recognition of handwritten digits. According to the results presented, it is seen that the detection and the recognition of characters is performed with greater accuracy using the KNN classifier and the performance is analysed.
