Mind the Gaps: A Curated Benchmark for Form Field Detection

2026-09-20

AI Summary

本文针对表单字段检测中的标注不一致问题，提出了一种高质量标注的数据集mini-CommonForms，并评估了现有检测方法。

Abstract

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 mini-CommonForms, a carefully curated FFD benchmark 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 in document automation and accessibility-oriented applications. Dataset and code are available at https://github.com/moured/mini-commonforms

Problem

• Form Field Detection
• annotation
• benchmark

Innovation

• Form Field Detection
• Benchmark Dataset
• High-Quality Annotations
• Reproducible Research