Image segmentation, classification and recognition methods for comics: A decade systematic literature review
R Sharma, V Kukreja�- Engineering Applications of Artificial Intelligence, 2024 - Elsevier
R Sharma, V Kukreja
Engineering Applications of Artificial Intelligence, 2024•ElsevierContext The field of comic recognition (CR) has gained significant attention in recent years
due to the growing popularity of digital comics. CR involves the automatic identification and
extraction of comic panels, speech bubbles, and text from digital comic images, which has
numerous applications in the digital media industry. Objective The objective of this review
paper is to conduct a systematic literature analysis of the current state of the art in CR
techniques and methods. The authors strive to present all major discoveries related to�…
due to the growing popularity of digital comics. CR involves the automatic identification and
extraction of comic panels, speech bubbles, and text from digital comic images, which has
numerous applications in the digital media industry. Objective The objective of this review
paper is to conduct a systematic literature analysis of the current state of the art in CR
techniques and methods. The authors strive to present all major discoveries related to�…
Context
The field of comic recognition (CR) has gained significant attention in recent years due to the growing popularity of digital comics. CR involves the automatic identification and extraction of comic panels, speech bubbles, and text from digital comic images, which has numerous applications in the digital media industry.
Objective
The objective of this review paper is to conduct a systematic literature analysis of the current state of the art in CR techniques and methods. The authors strive to present all major discoveries related to representation models, techniques, tools, datasets, and comparative evaluations of recognition models.
Methods
The present study utilizes the conventional Systematic Literature Review (SLR) approach and covers a wide range of 72 articles published in 17 prominent journals and 29 notable conferences and workshops.
Key findings
The available literature on recognition models and techniques is broadly categorized into three groups: computer vision (CV) technique being the most commonly used (75%) in the selected studies, followed by non-CV techniques (13.88%) and hybrid techniques (11.11%). Among the widely used datasets in the selected studies, Manga 109 and Ebdtheque are recognized by 50% of them. Within the field of CR, authors such as Jean-Christophe Burie, Christophe Rigaud, Jean-Marc Ogier, and Zhi Tang have received significant attention.
Conclusion
The paper summarizes the findings of the CR research and highlights the need for standardization of accuracy norms and datasets. It emphasizes the importance of exploring and developing hybrid techniques for efficient CR tasks.
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