CNN-Powered Monument Detection Webapp for Preserving India’s Cultural Heritage

Particularly in India, where a wide variety of monuments and historical sites are dispersed throughout the nation, cultural heritage protection has recently gained importance. This study introduces a web application for monument discovery powered by Convolutional Neural Networks (CNN) that provides a variety of elements that will aid in protecting India’s cultural heritage. CNNs are used by the app as the deep learning technology to precisely recognize and classify monuments based on photographs that user’s input. The web app’s user-friendly layout makes it accessible to those with little to no technological knowledge. It also provides a number of helpful features, such as local weather predictions and temperature information for users at the monument site. Additionally, the app has an interactive map that shows the monument’s precise location and address. When visiting historical places, these aspects help to provide guests a more thorough and immersive experience. The outcomes demonstrate how effective the CNN-based web application is at classifying monuments. A sizable collection of images of famous sites in India were used to test the proposed method. The system may be used for cultural preservation, tourism, and education. Overall, this study provides a novel approach for locating monuments and significantly contributes to preserving India’s rich cultural history. The accuracy of the proposed model is 92.14% in predicting monuments.

I. Introduction

India, with its rich cultural heritage, boasts a myriad of historical monuments that serve as testaments to the country’s glorious past [1]. However, the preservation of these invaluable landmarks is increasingly challenging due to neglect, vandalism, and the potential risks posed by natural disasters. To address this pressing issue and promote the preservation of India’s cultural heritage, we present a ground-breaking research endeavour focused on the development of a CNN-powered Monument Detection Web App [2].