Abstract:
Identification of an individual and gender from handwritten documents presents an intriguing
research problem for researchers, as there has been relatively little research in this area. This
research aims to examine a machine learning classification algorithm for recognizing the
attributes and handwritten characters of various authors. The study proposes a scheme for user
authentication that is based on data from pen tablets and handwriting. This research employed
two techniques to ascertain the author's identity and to recognize handwritten characters.
According to the study's analysis of experimental findings, the accuracy levels for SVM, LR,
LDA, and RF are 87% 85%, 73%, and 77%, respectively. Both SVM and LR have an accuracy
level greater than 80%.
