Generating Descriptive Captions for Images Using CNN and RNN

Computer vision is a discipline for creating captions for images is a significant challenge. Deep learning algorithms have demonstrated considerable potential for solving this issue in recent years. In particular, CNN (Convolutional Neural Network) for image recognition and LSTM (Long-Short Term Memory) networks for caption generation, this research suggests an innovative method for captioning images using deep learning. The Flickr 8k text folder is the dataset used for captions, while Flickr 8k Dataset is the dataset required for images. This research suggests data cleaning and filtering methods, such as multi-crop, multi-scale, and vertical mirror, to avoid overfitting. Moreover, Natural Language Processing (NLP) methods are used to enhance the generated captions, including tokenization and stop word removal. The experiment’s findings demonstrate that the recommended approach produces appropriate descriptions for images. The CNN and LSTM networks’ complementary qualities produce results that are both accurate and diverse in terms of generated captions. In order to improve the model’s accuracy, the study report suggests using larger datasets like Flicker 8K. The suggested method may be used for a variety of purposes, such as automatic social media post captioning and image captioning for those with vision impairments. The research provides an innovative and creative method for deep learning-based image captioning. The suggested method creates high-quality captions for photos with high accuracy and diversity by combining data cleaning and NLP approaches. To further increase the model’s accuracy, future study can look into the utilization of larger datasets and different models, such COCO (Common Objects in Context), can be implemented.