Vision-Language Models (VLMs) are increasingly used in document processing pipelines to convert flowchart images into structured code (e.g., Mermaid). In production, these systems process arbitrary inputs for which no ground-truth code exists, making output quality difficult to assess. We propose a reference-free evaluation framework that monitors flowchart image-to-code generation quality at inference time, using only the input image and the generated output. The framework introduces two automated metrics:
RecallOCR, which estimates content coverage by extracting text from the input image via OCR as a proxy reference, and
PrecisionVE, which detects hallucinated elements through Visual Entailment against the original image. Their harmonic mean,
F1OCR-VE, provides a unified quality score. Validation on the FlowVQA dataset shows strong agreement with ground-truth metrics (average Pearson's
r=0.97,
0.91, and
0.94 for Recall, Precision, and F1, respectively), confirming the framework's reliability as a practical, reference-free alternative for continuous quality monitoring in production settings.