Automated Identification of Indian Heritage Monuments using VGG16-Based Convolutional Neural Networks

Monument recognition is a challenging task in the domain of image classification. Different structure orientations play a significant part in recognizing monuments in photographs. This paper presents a novel technique for categorizing diverse monuments based on the characteristics of their photographs. The deep Convolution Neural Networks VGG16 model is utilized to extract representations. The model is trained on cropped photos of several Indian monuments, which show a wide range of geographic and cultural variety. A monument is a physical structure dedicated to a person, event, or purpose that was built or erected. The importance of this paper to finding and classifying historical monuments accurately without any issue. We used emerging technologies for this identification purpose. without any Machine Learning and Deep Learning, are improving, accelerating image identification development, and allowing computer vision to reach new heights. There is more coverage of international landmarks and monuments, necessitating the need to link a structure's physical presence to its digital presence. As a result, the monument's automated identification comes into action. Almost 100 percent accuracy was predicted using the VGG 16 Model on our proposed dataset.