  As computers have become efficient at understanding visual information and
transforming it into a written representation, research interest in tasks like
automatic image captioning has seen a significant leap over the last few years.
While most of the research attention is given to the English language in a
monolingual setting, resource-constrained languages like Bangla remain out of
focus, predominantly due to a lack of standard datasets. Addressing this issue,
we present a new dataset BAN-Cap following the widely used Flickr8k dataset,
where we collect Bangla captions of the images provided by qualified
annotators. Our dataset represents a wider variety of image caption styles
annotated by trained people from different backgrounds. We present a
quantitative and qualitative analysis of the dataset and the baseline
evaluation of the recent models in Bangla image captioning. We investigate the
effect of text augmentation and demonstrate that an adaptive attention-based
model combined with text augmentation using Contextualized Word Replacement
(CWR) outperforms all state-of-the-art models for Bangla image captioning. We
also present this dataset's multipurpose nature, especially on machine
translation for Bangla-English and English-Bangla. This dataset and all the
models will be useful for further research.
