构建407个真实表单数据集，评测检测填空元素的模型性能。
AutoFormBench: Benchmark Dataset for Automating Form Understanding
- 用多种YOLO模型与OpenCV对比，定位表单元素。
- YOLOv11在所有元素类型上F1和准确率均最优。
- 适合做表单自动化处理的研究者与工程师参考。
自动化处理政府表格、医疗记录和企业发票等结构化文档仍面临挑战，主要源于真实场景中布局变化多样。本文提出AutoFormBench，一个包含407个标注的真实世界表单数据集，涵盖政府、医疗和企业领域，用于训练和评估表单元素检测模型。系统比较了经典OpenCV方法与四种YOLO架构（YOLOv8、YOLOv11、YOLOv26-s、YOLOv26-l）在定位和分类可填写元素（如复选框、输入行、文本框）方面的表现，覆盖多种PDF文档类型。结果表明，YOLOv11在所有元素类别和容差水平下均表现出一致更优的F1分数和交并比（Jaccard）准确率。
原文摘要 · Abstract (English)
Automated processing of structured documents such as government forms, healthcare records, and enterprise invoices remains a persistent challenge due to the high degree of layout variability encountered in real-world settings. This paper introduces AutoFormBench, a benchmark dataset of 407 annotated real-world forms spanning government, healthcare, and enterprise domains, designed to train and evaluate form element detection models. We present a systematic comparison of classical OpenCV approaches and four YOLO architectures (YOLOv8, YOLOv11, YOLOv26-s, and YOLOv26-l) for localizing and classifying fillable form elements. specifically checkboxes, input lines, and text boxes across diverse PDF document types. YOLOv11 demonstrates consistently superior performance in both F1 score and Jaccard accuracy across all element classes and tolerance levels.
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