  In this work, we have introduced Gaussian Smoothen Semantic Features (GSSF)
for Better Semantic Selection for Indian regional language-based image
captioning and introduced a procedure where we used the existing translation
and English crowd-sourced sentences for training. We have shown that this
architecture is a promising alternative source, where there is a crunch in
resources. Our main contribution of this work is the development of deep
learning architectures for the Bengali language (is the fifth widely spoken
language in the world) with a completely different grammar and language
attributes. We have shown that these are working well for complex applications
like language generation from image contexts and can diversify the
representation through introducing constraints, more extensive features, and
unique feature spaces. We also established that we could achieve absolute
precision and diversity when we use smoothened semantic tensor with the
traditional LSTM and feature decomposition networks. With better learning
architecture, we succeeded in establishing an automated algorithm and
assessment procedure that can help in the evaluation of competent applications
without the requirement for expertise and human intervention.
