Image Based Indian Monument Recognition Using Convoluted Neural Networks (With Aws)

Project Code :TCMAPY959


The main objective of the project "Image Based Indian Monument Recognition Using Convolutional Neural Networks (with AWS)" is to develop a system that utilizes CNNs and AWS services to accurately identify and classify Indian monuments based on input images, facilitating automated recognition and categorization of diverse architectural landmarks in India. The project aims to leverage the power of deep learning and cloud infrastructure to create a robust and efficient solution for monument recognition, aiding in various applications such as tourism, cultural preservation, and historical research.


Monument recognition is a challenging problem in the domain of image classification due to huge variations in the architecture of different monuments. Different orientations of the structure play an important role in the recognition of the monuments in their images. The paper proposes an approach for classification of various monuments based on the features of the monument images. The state-of-the-art Deep Convolutional Neural Networks (DCNN) is used for extracting representations. The model is trained on representations of different Indian monuments, obtained from cropped images, which exhibit geographic and cultural diversity. Experiments have been carried out on the manually acquired dataset that is composed of images of different monuments where each monument has images from different angular views. The experiments show the performance of the model when it is trained on representations of cropped images of the various monuments. The overall accuracy achieved is 92.7%, using DCNN, for a total of 100 different monuments that have been considered in the dataset for classification.

Keywords: Indian monument, deep learning, convolutional neural network (CNN), minutiae.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram



H/W Specifications:

Processor: I5/Intel Processor

RAM  :  8GB (min)

Hard Disk :  128 GB

S/W Specifications:

Operating System : Windows 10

Server-side Script:Python 3.6

IDE:PyCharm, Jupyter notebook

Libraries Used: Numpy, IO, OS, Flask, Keras, pandas, tensorflow

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