An Online Reference-Free Evaluation Framework for Flowchart Image-to-Code Generation

arXiv:2602.13376v2 Announce Type: replace-cross Abstract: 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: ext{Recall}{ext{OCR}}, which estimates content coverage by extracting text from the input image via OCR as a proxy reference, and ext{Precision}{ext{VE}}, which detects hallucinated elements through Visual Entailment against the original image. Their harmonic mean, ext{F1}{ext{OCR-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.

Source: arXiv cs.AI | 2026-07-10