@article{Tumer2025, 
author = {Candan Tumer and Erdal Guvenoglu and Volkan Tunali},
title = {Robust Multi-Label Cartoon Character Classification on the Novel Kral Sakir Dataset Using Deep Learning Techniques},
year = {2025},
journal = {Computers, Materials & Continua},
volume = {85},
number = {3},
pages = {5135-5158},
keywords = {Cartoon character recognition, multi-label classification, deep learning, transfer learning, predictive modelling, artificial intelligence-enhanced (AI-Enhanced) systems, Kral Sakir dataset},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.067840},
doi = {10.32604/cmc.2025.067840},
abstract = {Automated cartoon character recognition is crucial for applications in content indexing, filtering, and copyright protection, yet it faces a significant challenge in animated media due to high intra-class visual variability, where characters frequently alter their appearance. To address this problem, we introduce the novel Kral Sakir dataset, a public benchmark of 16,725 images specifically curated for the task of multi-label cartoon character classification under these varied conditions. This paper conducts a comprehensive benchmark study, evaluating the performance of state-of-the-art pretrained Convolutional Neural Networks (CNNs), including DenseNet, ResNet, and VGG, against a custom baseline model trained from scratch. Our experiments, evaluated using metrics of F1-Score, accuracy, and Area Under the ROC Curve (AUC), demonstrate that fine-tuning pretrained models is a highly effective strategy. The best-performing model, DenseNet121, achieved an F1-Score of 0.9890 and an accuracy of 0.9898, significantly outperforming our baseline CNN (F1-Score of 0.9545). The findings validate the power of transfer learning for this domain and establish a strong performance benchmark. The introduced dataset provides a valuable resource for future research into developing robust and accurate character recognition systems.}
}