Monument Tracker: Deep Learning Approach for Indian Heritage

Abstract

Abstract: Monuments are physical structures built to commemorate a person or event. Their importance to the region necessitates their documentation and upkeep. Due to the many variations in how various monuments are built, monument recognition is a challenging task in the field of picture classification. The various angles of the building are critical in identifying the monuments in photographs. As more international landmarks and monuments are covered, there is a greater need to connect a structure's physical presence to its digital presence. As a result, the monument's automated recognition is enabled. Monuments represent the culturally rich legacy of people of all ethnicities, castes, and faiths. It reflects tremendous achievements in art and architecture, and it also serves as the backbone of the surrounding region's socioeconomic progress through tourism. As an important historical and cultural heritage asset, the monument must be digitally recognized and archived. The monument photographs should be identified and described to aid in the preservation of people's cultures from various locations. The goal of this project is to present a method for classifying different monuments based on the characteristics of the monument photographs. Machine Learning and Deep Learning are advancing, speeding up advances in image recognition and allowing computer vision to reach new heights. The results with Baseline Model had an overall accuracy of 73.2%. After using Transfer Learning, we achieved an overall accuracy of 94.5% with VGG16 Architecture, Inception with an accuracy of 91.2% and Resnet50 with an accuracy of 85.5%.