by Redefine Digital Art and Photography
Authors: Yeffry Handoko Putra; Rahma Wahdiniwaty; Rini Maulina; Noorihan Abdul Rahman; Zuriani Ahmad Zukarnain; Wan Fariza Abdul Rahman
DOI: 10.1109/INCITEST64888.2024.11121421
Abstract
Batik is an important part of Indonesia’s culture. It is known for its detailed repeating designs. Repeating designs are tough for designers, especially when they to mix in modern styles. Still, more people online are getting excited about making unique batik patterns. With new tech, like deep learning, things have changed. These models are doing better than the old ways for tasks like classifying images and recognizing objects. One cool tech, called pix2pix, helps turn one image into another using a method called Conditional Generative Adversarial Networks (cGAN). It uses data sets with images along with their edge maps that get pulled using techniques like Canny Edge Detection in OpenCV. This means that it can help create complex batik designs. This study also looks at Non-Fungible Tokens (NFTs) and how they can help sell and keep batik art safe. By turning batik designs into NFTs, artists can prove their work is accurate and connect with fans all over the world. This fresh idea not only keeps Indonesia’s rich culture alive but also lets people on the internet and batik lovers join in the creative fun. More chances exist for people to work together and explore digital art while saving their cultural heritage. Looking ahead, the Batik patterns created can become NFTs. This means that each Batik pattern’s uniqueness and ownership can be clearly shown through blockchain tech. It opens up a digital marketplace where folks can buy, sell, or collect these digital art pieces. Adding NFTs gives more value and realness to the Batik patterns made using advanced deep-learning methods. This broadens how this research could be used and its effect in the digital world. © 2024 IEEE.
Author keywords
batik; cultural preservation; deep learning; non-fungible tokens (NFTS)
Indexed keywords
Engineering controlled terms
Arts computing; Edge detection; Electronic commerce; Historic preservation; Learning systems; Photography
Engineering uncontrolled terms
Adversarial networks; Batik; Cultural preservation; Data set; Deep learning; Digital art; Digital photography; Indonesia; Neural-networks; Non-fungible token
Engineering main heading
Deep neural networks






