TY - GEN
T1 - Image To Text Description Platform For Yakshagana Images By Detecting Crown Using CNN
AU - Kiranraj, M.
AU - Murthy, Anantha
AU - Manish,
AU - Prathwini, Prathwini
AU - Savitha, G.
AU - Harshitha, G. M.
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This research called "Yakshakatha - Image to Text Platform for Yakshagana Images."It employs deep learning to classify crowns in Yakshagana Kirita using CNN classification. Yakshagana is a traditional South Indian art form, well known for its intricate costumes and headgear, posing specific challenges in identifying and classifying crowns. The system aims to classify various Kirita styles and provide detailed descriptions of the respective Yakshagana characters, or Vesha, offering valuable cultural and symbolic insights. The model was trained on a self-labelled dataset of Yakshagana images and used convolutional and pooling layers to capture fine details in crown designs. It achieved an impressive accuracy of 99%, ensuring precise identification and classification. Real-time predictions for the input images are made, which have predetermined mappings and description content for recognized characters. The system employs Google Gemini AI, in order to enrich the text description of the image by integrating cultural background. The responsive interface is provided for users on the image upload, real-time result, and enriched visual feedback using annotated output. This project combines modern image recognition with rich cultural content to preserve and promote the traditional heritage of Yakshagana. It will help the users to understand and appreciate the vibrancy of this art form.
AB - This research called "Yakshakatha - Image to Text Platform for Yakshagana Images."It employs deep learning to classify crowns in Yakshagana Kirita using CNN classification. Yakshagana is a traditional South Indian art form, well known for its intricate costumes and headgear, posing specific challenges in identifying and classifying crowns. The system aims to classify various Kirita styles and provide detailed descriptions of the respective Yakshagana characters, or Vesha, offering valuable cultural and symbolic insights. The model was trained on a self-labelled dataset of Yakshagana images and used convolutional and pooling layers to capture fine details in crown designs. It achieved an impressive accuracy of 99%, ensuring precise identification and classification. Real-time predictions for the input images are made, which have predetermined mappings and description content for recognized characters. The system employs Google Gemini AI, in order to enrich the text description of the image by integrating cultural background. The responsive interface is provided for users on the image upload, real-time result, and enriched visual feedback using annotated output. This project combines modern image recognition with rich cultural content to preserve and promote the traditional heritage of Yakshagana. It will help the users to understand and appreciate the vibrancy of this art form.
UR - https://www.scopus.com/pages/publications/105006474887
UR - https://www.scopus.com/pages/publications/105006474887#tab=citedBy
U2 - 10.1109/AIDE64228.2025.10987399
DO - 10.1109/AIDE64228.2025.10987399
M3 - Conference contribution
AN - SCOPUS:105006474887
T3 - 2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025 - Proceedings
SP - 589
EP - 594
BT - 2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025
Y2 - 6 February 2025 through 7 February 2025
ER -