In this work, we will see a scalable, integrated text-based image retrieval system (search engine) and evaluate its effectiveness. The search engine crawls and indexes all the pages in given domains, retrieves images found on the pages along with all the relevant keywords that can be used to identify the images. The keywords are loaded into a database along with several statistics indicating the location of the keywords in the page. Thumbnail versions of the images are downloaded to the server to save disk space. Several heuristics and metrics are used for identifying the images and their relevance. In this work, we put forward a model for a search engine where an image can be uploaded from the local database of the user to retrieve information from database loaded. This is similar to the traditional keyword search used by most of the search engines with the only difference being that here an image is uploaded as a query rather than textual keywords. The fact that the image being used as query makes the search ever more complicated as the content of the image needs to be analyzed and matched to find the information corresponding to the uploaded image. This is most apt for searching information about images of historical monuments, places or any specific place or thing that is identifiable. Image search engines that quantify the contents of an image are called Content-Based Image Retrieval (CBIR) systems. The term CBIR is commonly used in the academic literature, but in reality, it’s simply a fancier way of saying “image search engine”, with the added poignancy that the search engine is relying strictly on the contents of the image and not any textual annotations associated with the image.