Automatically describing the information of an image using properly constructed sentences is a tricky task in any language. However, it has the potential to have a significant effect by enabling visually challenged individuals to better understand their surroundings. This paper proposes an image captioning system that generates detailed captions and extracts text from an image, if any, and uses it as a part of the caption to provide a more precise description of the image. To extract the image features, the proposed model uses Convolutional Neural Networks (CNNs) followed by Long Short-Term Memory (LSTM) that generates corresponding sentences based on the learned image features. Further, using the text extraction module, the extracted text (if any) is included in the image description and the captions are presented in audio form. Publicly available benchmark datasets for image captioning like MS COCO, Flickr-8k, Flickr-30k have a variety of images, but they hardly have images that contain textual information. These datasets are not sufficient for the proposed model and this has resulted in the creation of a new image caption dataset that contains images with textual content. With the newly created dataset, comparative analysis of the experimental results is performed on the proposed model and the existing pre-trained model. The obtained experimental results show that the proposed model is equally effective as the existing one in subtitle image captioning models and provides more insights about the image by performing text extraction.