MythicVision: a deep learning powered mobile application for understanding Indian mythological deities using weight centric decision approach - Tauseef Khan; Aditya Nitin Patil; Aviral Singh; Gitesh Prashant Bhavsar; Kanakagiri Sujay Ashrith; Sachi Nandan Mohanty (Scientific Reports)
Abstract Indian mythology is a treasure trove of divine tales, yet a gap in understanding still exists between foreign tourists and the rich cultural heritage of Indian deities. To address the problem, this paper presents a deep learning-driven mobile application named “MythicVision” designed to help foreign tourists better understand India’s rich cultural heritage by recognizing and interpreting images of Indian mythological deities. At first, four state-of-the-art deep models have been trained and evaluated on a custom in-house dataset consists of 10,970 images of various Indian deities sourced from both natural scene and web images. Then, model-wise weights have been assigned by estimating the test accuracies obtained from test sets. In weight-centric decision mechanism, buckets of all possible classes of image object are updated by aggregating the corresponding model-weights if, the predicted class of the specific model matches any of the possible class. Finally, any possible output class with highest aggregated value is selected as final class of the image object. The whole framework is seamlessly integrated in an end-to-end web application for the ease of user convenience. Key features of “MythicVision” include model-wise weight computation and a weight-centric decision mechanism, which deliver more accurate results compared to traditional majority voting in multi-class image classification. The experimental findings demonstrate that developed framework produces an accuracy of 96% on in-house dataset. The designed MythicVision aims to recognize and classify real-time Indian deity images along with providing valuable information to the users about the deity. The developed web application along with source code and user guidelines have been publicly released in https://github.com/Adinp1213/MythicVision for academic, research and other non-commercial purposes.