  Bengali literature has a rich history of hundreds of years with luminary
figures such as Rabindranath Tagore and Kazi Nazrul Islam. However, analytical
works involving the most recent advancements in NLP have barely scratched the
surface utilizing the enormous volume of the collected works from the writers
of the language. In order to bring attention to the analytical study involving
the works of Bengali writers and spearhead the text generation endeavours in
the style of existing literature, we are introducing RabindraNet, a character
level RNN model with stacked-LSTM layers trained on the works of Rabindranath
Tagore to produce literary works in his style for multiple genres. We created
an extensive dataset as well by compiling the digitized works of Rabindranath
Tagore from authentic online sources and published as open source dataset on
data science platform Kaggle.

Computation and Language;Artificial Intelligence