Robust Multi-Label Cartoon Character Classification on the Novel Kral Sakir Dataset Using Deep Learning Techniques
Computers, Materials and Continua, cilt.85, sa.3, ss.5135-5158, 2025 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 85 Sayı: 3
- Basım Tarihi: 2025
- Doi Numarası: 10.32604/cmc.2025.067840
- Dergi Adı: Computers, Materials and Continua
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
- Sayfa Sayıları: ss.5135-5158
- Anahtar Kelimeler: artificial intelligence-enhanced (AI-Enhanced) systems, Cartoon character recognition, deep learning, Kral Sakir dataset, multi-label classification, predictive modelling, transfer learning
- Maltepe Üniversitesi Adresli: Evet
Özet
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.