Combined RNN Approach for Addressing Morphological, Syntactical, and Semantic Challenges in Image Captioning

Image caption generation is an emerging field that combines deep learning and natural language processing (NLP) techniques to generate descriptive and accurate captions for images automatically. This not only facilitates the efficient organization of digital files but also aids visually impaired individuals in understanding the context of images. This research investigates the application of state-of-the-art deep learning and NLP methods for image caption generation, specifically focusing on the InceptionV3 and GloVe (global vectors for word representation) models. The InceptionV3 model, a convolutional neural network (CNN) architecture designed for image classification tasks, has demonstrated a high level of accuracy in the ImageNet Large Scale Visual Recognition Challenge (78.3%). The Keras sequential model was utilized to create four dense layers for the image and caption models, employing the InceptionV3 architecture for image classification. This modular design allows for stacking multiple layers to create a highly effective model for classifying images. In parallel, this research explores the GloVe model, an unsupervised approach for generating word embeddings that capture semantic and syntactic meaning. GloVe achieved an accuracy of 75.0%, surpassing the scores of competing methods such as Word2Vec (72.3%) and FastText (69.3%). Through our investigation, the research analyzes the effectiveness of these methods in generating descriptive and accurate captions for images. Additionally, the paper also identifies the challenges and limitations of current approaches, such as handling rare words and improving the diversity and coherence of generated captions.