A mobile-optimized convolutional neural network approach for real-time batik pattern recognition

Abstract

This research focuses on preserving and sharing knowledge about Indonesian batik, a blend of art and technology symbolizing the nation's creativity. To address declining awareness of batik types, a mobile application is introduced for real-time recognition and classification of batik motifs. The goal is to maintain appreciation and understanding of this cultural heritage. Using the EfficientNet convolutional neural network (CNN) architecture, the study enhances model accuracy with effective scaling. A dataset of 1350 images representing 15 batik types supports robust model training and evaluation. Results demonstrate successful implementation, yielding an Android app capable of deep learning-based real-time recognition with an 83% accuracy rate. This innovation aims to empower users to identify and appreciate distinct batik types, ensuring cultural preservation for current and future generations.