• Maheshwarlal.K
• Gayathri Devi.S
• Hari Prasanth.G

The fusion of natural language processing (NLP) and deep neural networks (DNNs) has propelled advancements in automatic image captioning, addressing the intricate challenge of bridging the semantic gap between visual content and descriptive language. This project investigates and implements a sophisticated framework that amalgamates NLP and DNNs to generate accurate and contextually rich captions for images. The initial phase of the project involves a comprehensive exploration of existing image captioning methodologies, emphasizing the limitations in understanding complex visual scenes and contextual nuances present in images. Leveraging the power of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), the proposed system establishes a robust foundation for extracting high-level visual features and learning intricate linguistic patterns, respectively. The CNNs encode the salient visual features, while the RNNs decode these features into coherent and contextually appropriate textual descriptions. A pivotal element in the project involves pre-processing the image data to extract meaningful visual features through CNNs and leveraging pre-trained models. Simultaneously, the text data undergoes tokenization and sequence modeling to comprehend the sequential nature of language.

"Automatic Image Captioning With Natural Language Processing and Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.f396-f401, March-2024, Available :http://www.jetir.org/papers/JETIR2403549.pdf

"Automatic Image Captioning With Natural Language Processing and Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppf396-f401, March-2024, Available at : http://www.jetir.org/papers/JETIR2403549.pdf