Mind the Gaps: A Curated Benchmark for Form Field Detection

Form Field Detection (FFD) is a fundamental component of document understanding systems, enabling applications ranging from large-scale industrial digitization to accessible form interaction for automated analysis. Unlike conventional object detection tasks, FFD is inherently challenging because fields are often defined by layout structure and whitespace rather than visible foreground content. Existing large-scale datasets frequently rely on heuristic annotation pipelines, resulting in noisy and inconsistent labels that hinder reliable evaluation. In this work, we introduce a carefully curated mini-CommonForms benchmark for FFD with consistent, high-quality annotations, and present a detailed evaluation of state-of-the-art detection approaches. The benchmark is designed to support reproducible research and realistic systems in both document automation and accessibility-oriented systems. Dataset and code are available at https://github.com/moured/mini-commonforms.