AutoFormBench: Benchmark Dataset for Automating Form Understanding
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Automating Horizon Scanning in Future Studies
We introduce document retrieval and comment generation tasks for automating horizon scanning. This is an important task in the field of futurology that collects sufficient information for predicting drastic societal chan…
ArticlesComment GenerationRetrievalAutomating Mathematical Proof Generation Using Large Language Model Agents and Knowledge Graphs
Large Language Models have demonstrated remarkable capabilities in natural language processing tasks, including mathematical problem-solving that requires multi-step logical reasoning. However, challenges persist in auto…
Formal LogicKnowledge GraphsLanguage ModelingLanguage Modelling+5Neural Architecture Search: Insights from 1000 Papers
In the past decade, advances in deep learning have resulted in breakthroughs in a variety of areas, including computer vision, natural language understanding, speech recognition, and reinforcement learning. Specialized, …
Natural Language UnderstandingNeural Architecture Searchspeech-recognitionSpeech RecognitionDeepRTL: Bridging Verilog Understanding and Generation with a Unified Representation Model
Recent advancements in large language models (LLMs) have shown significant potential for automating hardware description language (HDL) code generation from high-level natural language instructions. While fine-tuning has…
Code GenerationSemantic SimilaritySemantic Textual SimilarityMDCrow: Automating Molecular Dynamics Workflows with Large Language Models
Molecular dynamics (MD) simulations are essential for understanding biomolecular systems but remain challenging to automate. Recent advances in large language models (LLM) have demonstrated success in automating complex …