Character classification framework based on support vector machine and k-nearest neighbour schemes

Abstract

ABSTRACT:The problem of Thai character classification can be difficult because of the large number of characters and the similarity in the shape of many characters. While previous work combined different fonts to build their classifier, this paper proposes a framework based on support vector machine (SVM) and k-NN schemes to exploit characteristics of each font separately. In this framework, each font is used to train an SVM separately. With the trained SVMs, a vector of predicted values can be produced for any input image. Then a class label of the input image can be found by a k-NN based scheme. The proposed framework performs well with familiar fonts while providing an acceptable performance on unfamiliar fonts.

Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine

“…40 In this binary classification problem, it is beneficial to set k to an odd number, so as to avoid tied votes. 41,42 The bootstrap method was used to set the optimal value of k . Euclidean distance was employed to measure the distance.…”