Journal of Multimedia Processing and Technologies Ricardo Rodríguez Jorge 17 2 2026 https://doi.org/10.6025/jmpt/2026/17/2/59-74 https://www.dline.info/jmpt/fulltext/v17n2/jmptv17n2_2.pdf This study presents a comparative analysis of meta-learning-driven approaches for few-shot font adaptation, addressing the critical challenge of recognizing unseen font styles with limited labeled samples. As data scarcity and domain shifts hinder conventional deep learning models, we investigate three adaptation paradigms: baseline fine tuning, metric based Prototypical Networks, and Model-Agnostic Meta-Learning (MAML). Through rigorous evaluation across 1-shot, 5-shot, and 10-shot settings, we assess classification accuracy, data efficiency, adaptation velocity, and knowledge retention. Empirical results demonstrate that MAML consistently achieves superior performance, attaining 50.9%, 58.3%, and 62.5% accuracy, respectively, while exhibiting the fastest convergence and lowest variance across target fonts. Feature representation analysis via t-SNE visualizations confirms that meta-learning facilitates effective domain alignment, transforming scattered target samples into compact, class-discriminative clusters. Furthermore, confusion matrix analysis reveals reduced inter-class confusion, particularly for structurally similar digits. Crucially, our forgetting measure indicates that MAML preserves source-domain knowledge with near-zero catastrophic forgetting, unlike conventional fine-tuning which suffers significant performance degradation. These findings establish meta learning as a principled framework that balances rapid adaptation, data efficiency, and long term stability. The proposed approach offers scalable solutions for real world applications that require continuous adaptation to novel visual styles under data constrained regimes, advancing fewshot learning methodologies in font recognition and broader low resource visual recognition tasks.