From Image Processing to Natural Language Generation: A Study on Image Captioning

Image captioning is an essential tool in modern times, and many built-in applications use deep neural network models to generate captions for images. The process of generating image captions entails detecting important objects, describing their characteristics, understanding their connections within the image, and producing sentences that are both grammatically and semantically accurate. In this paper, we propose a deep learning model that uses computer vision and machine translation to detect objects in images, recognize their relationships, and generate captions. Our model employs the Transfer Learning technique and the Flickr8k dataset, and we implemented it in Python3. We also provide details on the functions and structure of various neural networks used in our approach, such as CNN, RNN, and LSTM. Image caption generators have various applications in the fields of Computer Vision and Natural Language Processing. For instance, they are used in image segmentation, which is utilized by Facebook and Google Photos. These generators can even extend their use to video frames and can automate the process of image interpretation. Additionally, they have significant potential for aiding visually impaired individuals..