From ancient ages, artworks have been the object of research in artist identification.
Expert art historians primarily handle this issue manually. But an automatic artist
recognition system using artwork is compulsory to lower the error percentage, and
only a few progressive efforts are undertaken in this field, especially on Bangladeshi
Artists. Our Convolutional Neural Network (CNN) model aims to determine the
painter of a painting with a satisfactory accuracy standard. There are 450 paintings
from 6 well-known Bangladeshi artists comprised in our novel dataset. Two different
convolution kernels are used in model design, Model-1 has 3 X 3 convolutional
kernels, and Model-2 has 5 X 5 kernel size. Our models achieve significantly higher
classification accuracy as 87% for Model-1 and 89% for Model-2. Our result
evaluation demonstrates that CNNs is not merely a robust learning tool for artist
identification but also effective in predicting unique styles of an artisan.
