MERIT数据集：建模和有效地呈现可解释的转录本

IF 7.6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Pub Date : 2025-09-29 DOI:10.1016/j.patcog.2025.112502

Ignacio de Rodrigo, Alberto Sanchez-Cuadrado, Jaime Boal, Alvaro J. Lopez-Lopez

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引用次数: 0

摘要

本文介绍了MERIT数据集，这是一个多模态的、完全标记的学校成绩报告数据集。MERIT数据集包含超过400个标签和33k个样本，是训练模型的资源，用于要求视觉丰富的文档理解任务。它包含链接文本、视觉和布局域中的模式的多模态特性。MERIT数据集还以受控的方式包含偏差，使其成为对语言模型中引起的偏差进行基准测试的宝贵工具。本文概述了数据集的生成流程，并强调了其在不同领域的主要特征和模式。我们对令牌分类的数据集进行了基准测试，表明即使对于SOTA模型，它也构成了重大挑战。

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The MERIT dataset: Modelling and efficiently rendering interpretable transcripts

This paper introduces the MERIT Dataset, a multimodal, fully labeled dataset of school grade reports. Comprising over 400 labels and 33k samples, the MERIT Dataset is a resource for training models in demanding Visually-rich Document Understanding tasks. It contains multimodal features that link patterns in the textual, visual, and layout domains. The MERIT Dataset also includes biases in a controlled way, making it a valuable tool to benchmark biases induced in Language Models. The paper outlines the dataset’s generation pipeline and highlights its main features and patterns in its different domains. We benchmark the dataset for token classification, showing that it poses a significant challenge even for SOTA models.

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来源期刊

Pattern Recognition 工程技术-工程：电子与电气

CiteScore

14.40

自引率

16.20%

发文量

683

审稿时长

5.6 months

期刊介绍： The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.