Abstract
Optical Character Recognition (OCR) is an established task with the objective of identifying the text present in an image. While many off-the-shelf OCR models exist, they are often trained for either scientific (e.g., formulae) or generic printed English text. Extracting text from chemistry publications requires an OCR model that is capable in both realms. Nougat, a recent tool, exhibits strong ability to parse academic documents, but is unable to parse tables in PubMed articles, which comprises a significant part of the academic community and is the focus of this work. To mitigate this gap, we present the Printed English and Chemical Equations (PEaCE) dataset, containing both synthetic and real-world records, and evaluate the efficacy of transformer-based OCR models when trained on this resource. Given that real-world records contain artifacts not present in synthetic records, we propose transformations that mimic such qualities. We perform a suite of experiments to explore the impact of patch size, multi-domain training, and our proposed transformations, ultimately finding that models with a small patch size trained on multiple domains using the proposed transformations yield the best performance. Our dataset and code is available at this https URL.
Abstract (translated)
光字符识别（OCR）是一个已经确立的任务，旨在识别图像中的文本。虽然有许多现成的OCR模型存在，但它们通常是在科学（如公式）或通用打印英语文本上训练的。从化学出版物中提取文本需要一种在两种情况下都具有良好能力的OCR模型。 Nougat是一个最近的工具，表现出对学术文档的解析能力，但它无法解析PubMed文章中的表格，而这在学术社区中占有重要地位，也是本研究的核心。为了弥合这一差距，我们提出了Printed English and Chemical Equations（PEaCE）数据集，包含合成和真实世界记录，并评估了当用此资源训练Transformer-based OCR模型时，其有效性。鉴于真实世界记录包含在合成记录中不存在的 artifacts，我们提出了模拟这种特性的变换。我们进行了一系列实验，以探索补丁大小、多领域训练以及我们提出的变换对OCR模型的影响，最终发现，在补丁尺寸较小、跨领域训练并使用我们提出的变换训练的模型中，模型的性能最好。我们的数据和代码可以在该https URL上找到。
URL
https://arxiv.org/abs/2403.15724
