A comprehensive survey on image captioning: from handcrafted to deep learning-based techniques, a taxonomy and open research issues

Image captioning is a pretty modern area of the convergence of computer vision and natural language processing and is widely used in a range of applications such as multi-modal search, robotics, security, remote sensing, medical, and visual aid. The image captioning techniques have witnessed a paradigm shift from classical machine-learning-based approaches to the most contemporary deep learning-based techniques. We present an in-depth investigation of image captioning methodologies in this survey using our proposed taxonomy. Furthermore, the study investigates several eras of image captioning advancements, including template-based, retrieval-based, and encoder-decoder-based models. We also explore captioning in languages other than English. A thorough investigation of benchmark image captioning datasets and assessment measures is also discussed. The effectiveness of real-time image captioning is a severe barrier that prevents its use in sensitive applications such as visual aid, security, and medicine. Another observation from our research is the scarcity of personalized domain datasets that limits its adoption into more advanced issues. Despite influential contributions from several academics, further efforts are required to construct substantially robust and reliable image captioning models.