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

arXiv: https://arxiv.org/abs/2107.14070 · HTML: https://mlsanity.com/paper/2107.14070/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 89 references that are in the map.
- First posted on arXiv 29 Jul 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks](https://mlsanity.com/paper/1911.03852/) (2019)
- [Low-bit Quantization of Neural Networks for Efficient Inference](https://mlsanity.com/paper/1902.06822/) (2019)
- [Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices](https://mlsanity.com/paper/2105.10288/) (2021)
- [Bit Efficient Quantization for Deep Neural Networks](https://mlsanity.com/paper/1910.04877/) (2019)
- [A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints](https://mlsanity.com/paper/2011.14858/) (2020)
- [Rethinking Generalization in American Sign Language Prediction for Edge Devices with Extremely Low Memory Footprint](https://mlsanity.com/paper/2011.13741/) (2020)
- [Visual Landmark Recognition from Internet Photo Collections: A Large-Scale Evaluation](https://mlsanity.com/paper/1409.5400/) (2014)
- [Advances in Classifying the Stages of Diabetic Retinopathy Using Convolutional Neural Networks in Low Memory Edge Devices](https://mlsanity.com/paper/2106.01739/) (2021)

## Abstract (arXiv)

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.