Automatic Writer Identification of Historical Kannada Handwritten Palm Leaf Manuscripts using AlexNet Deep Learning Approach

Abstract

Ancient manuscripts have been a rich source of archeological information for decades, and some of the researchers have trying to develop Machine learning(ML) and Deep learning(DL) tools to restore the degraded information from ancient manuscripts. Today, the challenge lies in cataloging the manuscripts based on categories like subject, title, author, place, language, and script. The proposed study presents an automated deep learning model developed using the AlexNet CNN concept to classify and organize the historically significant Kannada handwritten manuscripts based on the various authors. Specifically, the AlexNet CNN approach is used to classify old Kannada handwritten manuscripts according to authorization. Old manuscripts present a unique set of challenges because they are historically significant, contain a variety of styles, and contain damaged text. The proposed research intends to present a strategy that employs deep learning techniques to attribute writing to older Kannada writings automatically. The proposed method shows promising results in several areas, i.e., the model's overall average of accuracy is 99.85%. The accurate assignment of publications to their respective authors. The classification model's performance for each class.