  Image Captioning is an arduous task of producing syntactically and
semantically correct textual descriptions of an image in natural language with
context related to the image. Existing notable pieces of research in Bengali
Image Captioning (BIC) are based on encoder-decoder architecture. This paper
presents an end-to-end image captioning system utilizing a multimodal
architecture by combining a one-dimensional convolutional neural network (CNN)
to encode sequence information with a pre-trained ResNet-50 model image encoder
for extracting region-based visual features. We investigate our approach's
performance on the BanglaLekhaImageCaptions dataset using the existing
evaluation metrics and perform a human evaluation for qualitative analysis.
Experiments show that our approach's language encoder captures the fine-grained
information in the caption, and combined with the image features, it generates
accurate and diversified caption. Our work outperforms all the existing BIC
works and achieves a new state-of-the-art (SOTA) performance by scoring 0.651
on BLUE-1, 0.572 on CIDEr, 0.297 on METEOR, 0.434 on ROUGE, and 0.357 on SPICE.
