Enhancing Tourism and Exploration Through Advanced Mobile Technology and Machine Learning

In response to the evolving tourism landscape, we present a mobile app that employs cutting-edge image recognition to identify nearby monuments, offering engaging historical and cultural insights. Additionally, it provides location-based recommendations for amenities, simplifying travel logistics. The user-friendly app supports multiple languages and has the potential to enhance tourists' engagement with monuments, shaping the future of travel experiences. We utilized a dataset of over 5,000 images of Indian monuments obtained from Kaggle and trained multiple convolutional neural network models to effectively categorize them into 24 different monument classes based on their architectural styles, regional origins, and historical periods. MobileNet V2 was the best choice for the image categorization challenge because it performed better than the other models tested, obtaining the greatest accuracy, BLEU score, and METEOR score. Conv2D is appropriate for less demanding applications since it achieves a decent mix between accuracy and computing efficiency, even though it cannot match MobileNet V2's precision. VGG 16 is a good choice for situations where caption quality is more important than classification accuracy since, despite its poorer accuracy when compared to Conv2D, it produces excellent captions. On the other hand, of the models, ResNet 50 had the lowest accuracy and BLEU score. In parallel research, we created a deep learning method that can recognize Indian historic monuments from photos on its own with an astounding 98.6% accuracy rate. This method has a lot of potential for both tourism and cultural preservation.