Handwritten Digit Recognition Using Structural, Statistical Features and K-nearest Neighbor Classifier

Abstract

-This paper presents a new approach to offline handwritten numeral recognition based on structural and statistical features. Five different types of skeleton features: (horizontal, vertical crossings, end, branch, and cross points), number of contours in the image, Widthto-Height ratio, and distribution features are used for the recognition of numerals. We create two vectors Sample Feature Vector (SFV) is a vector which contains Structural and Statistical features of MNIST sample data base of handwritten numerals and Test Feature Vector (TFV) is a vector which contains Structural and Statistical features of MNIST test database of handwritten numerals. The performance of digit recognition system depends mainly on what kind of features are being used. The objective of this paper is to provide efficient and reliable techniques for recognition of handwritten numerals. A Euclidian minimum distance criterion is used to find minimum distances and knearest neighbor classifier is used to classify the numerals. MNIST database is used for both training and testing the system. A total 5000 numeral images are tested, and the overall accuracy is found to be 98.42%.

A hybrid CNN–KNN approach for identification of COVID-19 with 5-fold cross validation

“…In proposed work, the value of K or the number of nearest neighbors is taken 3. Distance between the features specified for training and testing samples is calculated in the KNN [ 37 ]. The majority voting class of training features used in the KNN algorithm is centered on Euclidian distance measurement.…”