Robust Multi-Label Cartoon Character Classification on the Novel Kral Sakir Dataset Using Deep Learning Techniques

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.