Visual Literacy Reinvented: A CNN-LSTM Approach for E-Learning

This research aims to implement an innovative system that could able to deliver description of the image tailored for the educational sector, aiming to enhance the accessibility, and understanding of visual content in E-learning environments. Leveraging recent advancements in deep learning, the system uses methods like Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN). LSTM model will not only create the phrase but also summarize the text and provide a description that is appropriate for the image. CNN model is used to detect the items that are present in the image and uses some other high models to optimize the performance. The proposed solution holds promising applications in automated image tagging, content indexing, and improved accessibility for visually impaired learners. Through this integration, the project addresses the crucial need for meaningful image captions in educational materials, contributing to a more inclusive and enriched learning experience.