This work addresses the problem of searching and retrieving similar textual images based on the detected text and opens the new directions for textual image retrieval. For image retrieval, several methods have been proposed to extract visual features and social tags; however, to extract embedded and scene text within images and use that text as automatic keywords/tags is still a young research field for text-based and content-based image retrieval applications. The automatic text detection retrieval is an emerging technology for robotics and artificial intelligence. In this study, the authors have proposed a novel approach to detect the text in an image and exploit it as keywords and tags for automatic text-based image retrieval. First, text regions are detected using maximally stable extremal region algorithm. Second, unwanted false positive text regions are eliminated based on geometric properties and stroke width transform. Next, the true text regions are proceeded into optical character recognition for recognition. Third, keywords are formed using a neural probabilistic language model. Finally, the textual images are indexed and retrieved based on the detected keywords. The experimental results on two benchmark datasets show the dominancy of text is efficient and valuable for image retrieval specifically for textual images.