This research addresses a critical challenge in the field of generative models, particularly in the generation and evaluation of synthetic images. Given the inherent complexity of generative models and the absence of a standardized procedure for their comparison, our study introduces a pioneering algorithm to objectively assess the realism of synthetic images. This approach significantly enhances the evaluation methodology by refining the Fr\'echet Inception Distance (FID) score, allowing for a more precise and subjective assessment of image quality. Our algorithm is particularly tailored to address the challenges in generating and evaluating realistic images of Arabic handwritten digits, a task that has traditionally been near-impossible due to the subjective nature of realism in image generation. By providing a systematic and objective framework, our method not only enables the comparison of different generative models but also paves the way for improvements in their design and output. This breakthrough in evaluation and comparison is crucial for advancing the field of OCR, especially for scripts that present unique complexities, and sets a new standard in the generation and assessment of high-quality synthetic images.

翻译：本研究聚焦生成模型领域的关键挑战，尤其是合成图像的生成与评估问题。鉴于生成模型固有的复杂性及缺乏标准化比较流程，我们提出一种开创性算法，用于客观评估合成图像的真实性。该方法通过优化弗雷歇初始距离（Fréchet Inception Distance, FID）评分，显著改进了评估体系，实现了对图像质量更精准、更具可解释性的评判。该算法专为解决阿拉伯手写数字真实图像生成与评估的难题而设计——由于图像生成中真实性的主观特性，该任务长期以来近乎无法实现。通过提供系统化客观框架，本方法不仅实现了不同生成模型间的比较，更为模型设计与产出的优化开辟了路径。这一评估与比较领域的突破对推动OCR技术发展至关重要，尤其适用于具有独特复杂性的文字系统，并为高质量合成图像的生成与评估树立了新标准。