Image Captioning Using Deep Learning Models
Ravi Kant Kumar, Dinesh Kumar, Ahmad Saeed · Computer Science and Engineering · 2024
Data Science and Artificial Intelligence(AI) have wide use cases across the industry, we wrote this paper to highlight the use of deep learning models in Image Caption generators. Thanks to deep learning, the combination of computer vision and natural language processing in Artificial intelligence has induced a lot of interest in research in recent years. The context of a photograph is automatically described in simple english. When a picture is captioned, the model learns to interpret the visual information of the image using one or more phrases. The ability to analyze the state, properties, and relationship between these objects is required for the meaningful description generation process of high-level picture semantics. In this paper, we are using CNN - LSTM architectural models on the captioning of a graphical image, and we hope to detect things and inform people via text messages in this research. To correctly identify the items, the input image is first reduced to grayscale and then processed by a Convolution Neural Network (CNN). The flickr-image-dataset was used. In this project, I have followed a variety of important concepts of image captioning and its standard processes, as this work develops a generative CNN-LSTM model that outperforms earlier baselines.