Open AccessA Simple Feature Extraction Method for Analysis of Hand Written Characters
Author(s) -
S. Vijayprasath
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1717/1/012066
Subject(s) - pattern recognition (psychology) , artificial intelligence , feature extraction , computer science , segmentation , artificial neural network , classifier (uml) , false alarm , feature (linguistics) , character recognition , character (mathematics) , set (abstract data type) , speech recognition , mathematics , image (mathematics) , philosophy , linguistics , geometry , programming language
The printed and handwritten alphabets as character recognition by feature extraction are described during this proposed work. A predefined set of 108 samples each containing twenty-six alphabets taken by paper handwritten are taken as set for training. The system portrayed is a good alternative solution for HCR & plays well with detection of handwritten characters. Here, the method experiences binarization and pre-processing for the segmentation stage. Individual characters after segmentation undergo feature extraction part. The artificial neural network is trained using the extracted features. A simple ANN is used as a classifier utilizing the average epochs and MSE for detecting the HCR recognition without a false alarm.