  Despite being one of the most spoken languages in the world ($6^{th}$ based
on population), research regarding Bengali handwritten grapheme (smallest
functional unit of a writing system) classification has not been explored
widely compared to other prominent languages. Moreover, the large number of
combinations of graphemes in the Bengali language makes this classification
task very challenging. With an effort to contribute to this research problem,
we participate in a Kaggle competition \cite{kaggle_link} where the challenge
is to separately classify three constituent elements of a Bengali grapheme in
the image: grapheme root, vowel diacritics, and consonant diacritics. We
explore the performances of some existing neural network models such as
Multi-Layer Perceptron (MLP) and state of the art ResNet50. To further improve
the performance we propose our own convolution neural network (CNN) model for
Bengali grapheme classification with validation root accuracy 95.32\%, vowel
accuracy 98.61\%, and consonant accuracy 98.76\%. We also explore Region
Proposal Network (RPN) using VGGNet with a limited setting that can be a
potential future direction to improve the performance.
