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arXiv · 2107.14070

Machine Learning Advances aiding Recognition and Classification of Indian Monuments and Landmarks

Aditya Jyoti Paul·Smaranjit Ghose·Kanishka Aggarwal·Niketha Nethaji·Shivam Pal·Arnab Dutta Purkayastha
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Abstract

Tourism in India plays a quintessential role in the country's economy with an estimated 9.2% GDP share for the year 2018. With a yearly growth rate of 6.2%, the industry holds a huge potential for being the primary driver of the economy as observed in the nations of the Middle East like the United Arab Emirates. The historical and cultural diversity exhibited throughout the geography of the nation is a unique spectacle for people around the world and therefore serves to attract tourists in tens of millions in number every year. Traditionally, tour guides or academic professionals who study these heritage monuments were responsible for providing information to the visitors regarding their architectural and historical significance. However, unfortunately this system has several caveats when considered on a large scale such as unavailability of sufficient trained people, lack of accurate information, failure to convey the richness of details in an attractive format etc. Recently, machine learning approaches revolving around the usage of monument pictures have been shown to be useful for rudimentary analysis of heritage sights. This paper serves as a survey of the research endeavors undertaken in this direction which would eventually provide insights for building an automated decision system that could be utilized to make the experience of tourism in India more modernized for visitors.

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Aditya Jyoti Paul, Smaranjit Ghose, Kanishka Aggarwal, Niketha Nethaji, Shivam Pal, Arnab Dutta Purkayastha. 2021-07-29. Machine Learning Advances aiding Recognition and Classification of Indian Monuments and Landmarks. https://arxiv.org/abs/2107.14070

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