  Generating fine-grained, realistic images from text has many applications in
the visual and semantic realm. Considering that, we propose Bangla Attentional
Generative Adversarial Network (AttnGAN) that allows intensified, multi-stage
processing for high-resolution Bangla text-to-image generation. Our model can
integrate the most specific details at different sub-regions of the image. We
distinctively concentrate on the relevant words in the natural language
description. This framework has achieved a better inception score on the CUB
dataset. For the first time, a fine-grained image is generated from Bangla text
using attentional GAN. Bangla has achieved 7th position among 100 most spoken
languages. This inspires us to explicitly focus on this language, which will
ensure the inevitable need of many people. Moreover, Bangla has a more complex
syntactic structure and less natural language processing resource that
validates our work more.
