Handwritten Digit Recognition Using K-Nearest Neighbour Classifier

被引：25

作者：

Babu, U. Ravi ^{[1
]}

Venkateswarlu, Y. ^{[2
]}

Chintha, Aneel Kumar ^{[3
]}

机构：

[1] Aacharya Nagarjuna Univ, Rajahmundry, AP, India

[2] GIET Engg Coll, Dept CSE, Rajahmundry, Andhra Pradesh, India

[3] GIET, MTech CSE, Rajahmundry, Andhra Pradesh, India

来源：

2014 WORLD CONGRESS ON COMPUTING AND COMMUNICATION TECHNOLOGIES (WCCCT 2014) | 2014年

关键词：

Structural features; fill hole density; digit recognition; profile distance; handwritten digits; CHARACTER-RECOGNITION;

D O I：

10.1109/WCCCT.2014.7

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper presents a new approach to off-line handwritten digit recognition based on structural features which is not required thinning operation and size normalization technique. In this paper uses four different types of structural features namely, number of holes, water reservoirs in four directions, maximum profile distances in four directions, and fill-hole density for the recognition of digits. The digit recognition system mainly depends on which kinds of features are used. The main objective of this paper is to provide efficient and reliable techniques for recognition of handwritten digits. A Euclidean minimum distance criterion is used to find minimum distances and k-nearest neighbor classifier is used to classify the digits. A MNIST database is used for both training and testing the system. 5000 images are used to test the proposed method a total 5000 numeral images are tested and got 96.94% recognition rate.

引用

页码：60 / +

页数：2

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