Open AccessClassification and recognition of online hand-written alphabets using Machine Learning Methods
Author(s) -
Renu Popli,
Isha Kansal,
Atul Garg,
Nitin Goyal,
Kanika Garg
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1022/1/012111
Subject(s) - computer science , alphabet , artificial intelligence , identification (biology) , pattern recognition (psychology) , matlab , machine learning , speech recognition , philosophy , linguistics , botany , biology , operating system
The hand-written alphabet recognition and classification plays an important role in pattern recognition, computer vision as well as image processing. In last few decades, a plethora of applications based on this area are developed such as sign identification, multi lingual learning systems etc. This paper classifies samples of hand-written alphabets into different classes using various machine learning methods. The challenging factor in hand written alphabets recognition lie in variations of style, shape and size of the letters. In this paper a simplified and accurate methodology is proposed based upon engineered features which are evaluated and tested using MatLab tool in comparison to other existing methods. The proposed system achieves a substantial amount of accuracy of 98% as compared to the state of the art approaches.