Open Access

ARTICLE

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

Candan Tumer¹, Erdal Guvenoglu², Volkan Tunali^3,*

1 Graduate School, Maltepe University, Istanbul, 34857, Turkiye
2 Department of Computer Programming, Vocational School, Maltepe University, Istanbul, 34857, Turkiye
3 Division of Computing, School of Computing, Engineering and Physical Sciences, University of the West of Scotland, London Campus, London, E14 2BE, UK

* Corresponding Author: Volkan Tunali. Email:

(This article belongs to the Special Issue: Advancements and Challenges in Artificial Intelligence, Data Analysis and Big Data)

Computers, Materials & Continua 2025, 85(3), 5135-5158. https://doi.org/10.32604/cmc.2025.067840

Received 14 May 2025; Accepted 19 August 2025; Issue published 23 October 2025

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.

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Keywords

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