  Extracting context from visual representations is of utmost importance in the
advancement of Computer Science. Representation of such a format in Natural
Language has a huge variety of applications such as helping the visually
impaired etc. Such an approach is a combination of Computer Vision and Natural
Language techniques which is a hard problem to solve. This project aims to
compare different approaches for solving the image captioning problem. In
specific, the focus was on comparing two different types of models:
Encoder-Decoder approach and a Multi-model approach. In the encoder-decoder
approach, inject and merge architectures were compared against a multi-modal
image captioning approach based primarily on object detection. These approaches
have been compared on the basis on state of the art sentence comparison metrics
such as BLEU, GLEU, Meteor, and Rouge on a subset of the Google Conceptual
captions dataset which contains 100k images. On the basis of this comparison,
we observed that the best model was the Inception injected encoder model. This
best approach has been deployed as a web-based system. On uploading an image,
such a system will output the best caption associated with the image.
