Year: 2025
Type: article
Abstract: : There have been groundbreaking applications that connect visual content understanding with verbal expression made possible by the rise of Deep Learning (DL) in Computer Vision (CV) and Natural Language Processing (NLP). Among these, the project on Image Caption Generator using Long Short-Term Memory (LSTM) networks stands out as a significant advancement. This research aims to develop a system that can automatically generate descriptive and contextually relevant captions for a wide array of images. By leveraging LSTM, a type of recurrent neural network, the model captures the intricate dynamics between visual cues and their linguistic descriptions, enabling it to understand and describe complex scenes with accuracy.The proposed solution involves curating a diverse dataset of images annotated with captions, preprocessing this data to suit the model's requirements, and implementing the LSTM network to sequentially process image features and generate corresponding text. To train the model, we use an appropriate loss function and optimization strategies to reduce the gap between the produced captions and the real annotations. Using this method, you can be confident that the captions will be precise and appropriate for the pictures.The versatility and robustness of the proposed Image Caption Generator (ICG) underline its potential to serve multiple industries, including social media, e-commerce, healthcare, and education, among others. As it advances, it promises to not only improve user experiences across digital environments but also contribute to the broader goals of making technology more intuitive and inclusive.
Author: V T Ram Pavan Kumar M
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409404286