A Deep Learning Approach for Automated Image Captioning with CNN and LSTM Models

Accurately describing a picture has turned out to be a crucial problem, and expert system researchers have always been interested in image captioning, sometimes referred to as characterizing the image. Creating an image caption generator entail describing the features and qualities of the picture. Numerous fields, including robotic vision, business, and storytelling via album uploads, can profit from using it. The goal of this study is to caption photos using computer vision and machine translation. It entails identifying the actions, objects, and properties in an image as well as the relationship between the objects and the descriptions that are generated. The majority of them make use of encoder-decoder frameworks, in which an input image is decoded into a string of evocative text and descriptions after being encoded to a representation of the image's information in the center. Python is the programming language and the Flickr8k dataset is used for the same. Using Flutter, the project entails creating an application that takes an input image, extracts its features, and produces precise descriptions. Those who are blind or visually handicapped could benefit greatly from it. With applications in customer service, education, healthcare, and career support, Chatbots are becoming increasingly significant entry points to online information and services. The proposed system has achieved a Bilingual Evaluation Understudy BLEU - 1 score of 0.716880 and BLEU - 2 score of 0.593009. Furthermore, before chatbots can realize their full potential, a number of issues need to be fixed. To support the growth of knowledge in this emerging subject, we propose a research agenda in the form of future directions and concerns to be addressed by chatbot research. This proposal compiles years of discussions at the CONVERSATIONS workshop series on chatbot research.