Post-OCR Document Correction with large Ensembles of Character Sequence-to-Sequence Models
Department of Computer Science, Dalhousie University（达尔豪斯大学计算机科学系）；Institute of Research in Social Sciences and Humanities (ICSOH), Universidad Nacional de Salta - CONICET（萨尔塔国立大学社会科学与人文研究所（ICSOH）- CONICET）；Department of Computer Science and Engineering, Universidad Nacional del Sur（南方国立大学计算机科学与工程系）；Institute for Computer Science and Engineering, UNS–CONICET（UNS–CONICET 计算机科学与工程研究所）
Juan Ramirez-Orta, Eduardo Xamena, Ana Maguitman, Evangelos Milios, Axel J. Soto
In this paper, we propose a novel method based on character sequence-to-sequence models to correct documents already processed with Optical Character Recognition (OCR) systems. The main contribution of this paper is a set of strategies to accurately process strings much longer than the ones used to train the sequence model while being sample- and resource-efficient, supported by thorough experimentation. The strategy with the best performance involves splitting the input document in character n-grams and combining their individual corrections into the final output using a voting scheme that is equivalent to an ensemble of a large number of sequence models. We further investigate how to weigh the contributions from each one of the members of this ensemble. We test our method on nine languages of the ICDAR 2019 competition on post-OCR text correction and achieve a new state-of-the-art performance in five of them. Our code for post-OCR correction is shared at https://github.com/jarobyte91/post_ocr_correction.
Dublin City University（都柏林城市大学）；Trinity College Dublin（都柏林圣三一学院）
Optical character recognition (OCR) for historical documents is a complex procedure subject to a unique set of material issues, including inconsistencies in typefaces and low quality scanning. Consequently, even the most sophisticated OCR engines produce errors. This paper reports on a tool built for postediting the output of Tesseract, more specifically for correcting common errors in digitized historical documents. The proposed tool suggests alternatives for word forms not found in a specified vocabulary. The assumed error is replaced by a presumably correct alternative in the post-edition b…
In this paper we describe a dataset of German and Latin \textit{ground truth} (GT) for historical OCR in the form of printed text line images paired with their transcription. This dataset, called \textit{GT4HistOCR}, consists of 313,173 line pairs covering a wide period of printing dates from incunabula from the 15th century to 19th century books printed in Fraktur types and is openly available under a CC-BY 4.0 license. The special form of GT as line image/transcription pairs makes it directly usable to train state-of-the-art recognition models for OCR software employing recurring neural netw…
Carnegie Mellon University（卡内基梅隆大学）；University of British Columbia（不列颠哥伦比亚大学）；George Mason University（乔治梅森大学）
There has been recent interest in improving optical character recognition (OCR) for endangered languages, particularly because a large number of documents and books in these languages are not in machine-readable formats. The performance of OCR systems is typically evaluated using automatic metrics such as character and word error rates. While error rates are useful for the comparison of different models and systems, they do not measure whether and how the transcriptions produced from OCR tools are useful to downstream users. In this paper, we present a human-centric evaluation of OCR systems, …
Document digitization is essential for the digital transformation of our societies, yet a crucial step in the process, Optical Character Recognition (OCR), is still not perfect. Even commercial OCR systems can produce questionable output depending on the fidelity of the scanned documents. In this paper, we demonstrate an effective framework for mitigating OCR errors for any downstream NLP task, using Named Entity Recognition (NER) as an example. We first address the data scarcity problem for model training by constructing a document synthesis pipeline, generating realistic but degraded data wi…
Improving MLLM's Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiency
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Zhongke Fanyu Technology Co., Ltd（中科泛语科技有限公司）
Documents are a core part of many businesses in many fields such as law, finance, and technology among others. Automatic understanding of documents such as invoices, contracts, and resumes is lucrative, opening up many new avenues of business. The fields of natural language processing and computer vision have seen tremendous progress through the development of deep learning such that these methods have started to become infused in contemporary document understanding systems. In this survey paper, we review different techniques for document understanding for documents written in English and con…
LeakageBench: Document-Level Leakage Risk for Redacting Personally Identifiable Information in Document Images
Center for Artificial Intelligence and Robotics (CAIRO)（人工智能与机器人中心（开罗））；Technical University of Applied Sciences Würzburg-Schweinfurt (THWS)（维尔茨堡-施韦因富特应用技术大学）；DataX
Indian Institute of Information Technology Allahabad（印度理工学院阿拉哈巴德分校）
In today's technological era, document images play an important and integral part in our day to day life, and specifically with the surge of Covid-19, digitally scanned documents have become key source of communication, thus avoiding any sort of infection through physical contact. Storage and transmission of scanned document images is a very memory intensive task, hence compression techniques are being used to reduce the image size before archival and transmission. To extract information or to operate on the compressed images, we have two ways of doing it. The first way is to decompress the im…
Most low-resource languages do not have the necessary resources to create even a substantial monolingual corpus. These languages may often be found in government proceedings but mainly in Portable Document Format (PDF) that contains legacy fonts. Extracting text from these documents to create a monolingual corpus is challenging due to legacy font usage and printer-friendly encoding, which are not optimized for text extraction. Therefore, we propose a simple, automatic, and novel idea that can scale for Tamil, Sinhala, English languages, and many documents along with parallel corpora. Since Tam…
OCR Context-Sensitive Error Correction Based on Google Web 1T 5-Gram Data Set
LACSC – Lebanese Association for Computational Sciences（黎巴嫩计算科学协会（LACSC））
自计算机时代开始以来，信息已经以数字方式表示，以便可以由电子计算机处理。当时，纸质书籍和文件大量出版，因此需要将其转换为数字格式。OCR是光学字符识别的缩写，其目的是将纸质图书转换为数字电子图书。令人遗憾的是，OCR系统仍然是错误和不准确的，因为它们在识别的文本中产生拼写错误，特别是当源文档的打印质量较低时。本文提出了一种OCR后处理上下文相关纠错方法，用于检测和纠正非字和实字OCR错误。该方法的基础是使用Google Web 1 T 5-gram数据集作为单词字典来检查OCR文本。Google数据集包含了非常大的词汇表和完全从互联网上获得的单词统计数据，使其成为执行基于词典的错误纠正的可靠来源。所提出的解决方案的核心是三种算法的组合：错误检测，候选拼写生成器和纠错算法，它们都利用从Google Web 1 T 5-gram数据集提取的信息。在不同语言的扫描图像上进行的实验表明，OCR纠错率有了很大的提高。作为未来的发展，所提出的算法是并行化，以支持并行和分布式计算架构。
PEaCE: A Chemistry-Oriented Dataset for Optical Character Recognition on Scientific Documents
College of Information Sciences and Technology, The Pennsylvania State University（宾夕法尼亚州立大学信息科学与技术学院）；Department of Chemical Engineering, The Pennsylvania State University（宾夕法尼亚州立大学化学工程系）；L3S Research Center, Leibniz University Hannover（汉诺威莱布尼茨大学L3S研究中心）；Department of Energy and Mineral Engineering, The Pennsylvania State University（宾夕法尼亚州立大学能源与矿物工程系）；Institutes of Energy and the Environment, The Pennsylvania State University（宾夕法尼亚州立大学能源与环境研究所）
