Image Talk: A Model for Image Caption Generation with Voice

The automatic creation of written descriptions from photographs represents a crucial point of interaction between computer vision and Natural Language Processing (NLP) in the field of artificial intelligence (AI). This research paper presents an innovative approach to image caption generation employing a hybrid neural architecture that amalgamates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. This novel methodology pushes the boundaries of image captioning techniques by incorporating cutting-edge techniques like Word2Vec embedding, ensuring the representation of semantic relationships between words in the generated captions. By leveraging the Xception model for robust feature extraction, this study advances the current state-of-the-art in image captioning methodologies. To quantify the efficacy of the proposed approach, comprehensive evaluations are conducted across diverse datasets including MS-COCO. These evaluations encompass rigorous assessment of both the accuracy of the generated captions in depicting the visual context and the fluency of the language employed. Performance metrics including BLEU (Bilingual Evaluation Understudy), METEOR, CIDER and ROUGE are utilized to quantitatively gauge the quality of the generated captions. The integration of a Text-to-Speech (TTS) feature for image captions is an additional enhancement to the proposed model.