Open AccessHand written character recognition using SVM
Author(s) -
Ammar Tahir,
Adil Pervaiz
Publication year - 2020
Publication title -
pacific international journal
Language(s) - English
Resource type - Journals
eISSN - 2663-8991
pISSN - 2616-4825
DOI - 10.55014/pij.v3i2.98
Subject(s) - support vector machine , artificial intelligence , pattern recognition (psychology) , computer science , k nearest neighbors algorithm , classifier (uml) , artificial neural network , machine learning
Classification is one of the most important tasks for different applications such as text categorization, tone recognition, image classification, microarray gene expression, proteins structure predictions, data Classification, etc. Hand-written digit classification is a process that interprets handwritten digits by machine. There are many techniques used for HRC like neural networks and k-nearest neighbor (KNN).In this paper, a novel supervised learning technique, Support Vector Machine (SVM), is applied to blur images data. SVM is a powerful machine model use for classification for two or more classes. This paper represents pixel base detection technique for training machines on blur images. SVM is employed as classifier results are accurate nearest 80% which are comparable with state of art.