  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.
