  Automatic image caption generation aims to produce an accurate description of
an image in natural language automatically. However, Bangla, the fifth most
widely spoken language in the world, is lagging considerably in the research
and development of such domain. Besides, while there are many established data
sets to related to image annotation in English, no such resource exists for
Bangla yet. Hence, this paper outlines the development of "Chittron", an
automatic image captioning system in Bangla. Moreover, to address the data set
availability issue, a collection of 16,000 Bangladeshi contextual images has
been accumulated and manually annotated in Bangla. This data set is then used
to train a model which integrates a pre-trained VGG16 image embedding model
with stacked LSTM layers. The model is trained to predict the caption when the
input is an image, one word at a time. The results show that the model has
successfully been able to learn a working language model and to generate
captions of images quite accurately in many cases. The results are evaluated
mainly qualitatively. However, BLEU scores are also reported. It is expected
that a better result can be obtained with a bigger and more varied data set.
