DocBank: A Benchmark Dataset for Document Layout Analysis

Li, Minghao; Xu, Yanjin; Cui, Lei; Huang, Shaohan; Wei, Furu; Li, Zhoujun; Zhou, Ming

doi:10.18653/v1/2020.coling-main.82

Cited by 144 publications

(94 citation statements)

References 38 publications

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“…Another dataset to solve document layout analysis is released by Li et al [13]. The dataset is known as DocBank, which is the extended version of the TableBank dataset [46].…”

Section: Docbankmentioning

confidence: 99%

“…In this survey paper, we have presented a thorough analysis of the recent state-of-theart approaches that have approached the problem of graphical page object detection in scanned document images by employing deep neural networks. Since page objects can be of several types [13], we have covered the three most important page objects in document images [9]. These graphical page objects are referred to as table, formulas, and figures.…”

Section: Introductionmentioning

confidence: 99%

“…At present, several datasets only focus on a single graphical page object [38,46,52]. Therefore, there is a growing need for large-scale datasets that provide annotations for multiple page objects like figures, formulas, and tables [5,13].…”

mentioning

confidence: 99%

“…It is important to mention that we have considered equation and formula as semantically equal in this table. Some of these datasets contain as many as 12 page objects[13]. For the sake of convenience, we have only includedtable, figure, and formula.…”

mentioning

confidence: 99%

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A Survey of Graphical Page Object Detection with Deep Neural Networks

et al. 2021

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In any document, graphical elements like tables, figures, and formulas contain essential information. The processing and interpretation of such information require specialized algorithms. Off-the-shelf OCR components cannot process this information reliably. Therefore, an essential step in document analysis pipelines is to detect these graphical components. It leads to a high-level conceptual understanding of the documents that make the digitization of documents viable. Since the advent of deep learning, deep learning-based object detection performance has improved many folds. This work outlines and summarizes the deep learning approaches for detecting graphical page objects in document images. Therefore, we discuss the most relevant deep learning-based approaches and state-of-the-art graphical page object detection in document images. This work provides a comprehensive understanding of the current state-of-the-art and related challenges. Furthermore, we discuss leading datasets along with the quantitative evaluation. Moreover, it discusses briefly the promising directions that can be utilized for further improvements.

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“…Another dataset to solve document layout analysis is released by Li et al [13]. The dataset is known as DocBank, which is the extended version of the TableBank dataset [46].…”

Section: Docbankmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A Survey of Graphical Page Object Detection with Deep Neural Networks

et al. 2021

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“…The study [11] developed token-level annotations on a scientific articles dataset named DocBank, used for the article metadata extraction and document layout analysis tasks. It consists of 500 thousand document pages with the label annotations such as Abstract, Author, Caption, Figure , and few others.…”

Section: A Standard Datasetsmentioning

confidence: 99%

Multi-Layout Unstructured Invoice Documents Dataset: A Dataset for Template-Free Invoice Processing and Its Evaluation Using AI Approaches

2021

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The daily transaction of an organization generates a vast amount of unstructured data such as invoices and purchase orders. Managing and analyzing unstructured data is a costly affair for the organization. Unstructured data has a wealth of hidden valuable information. Extracting such insights automatically from unstructured documents can significantly increase the productivity of an organization. Thus, there is a huge demand to develop a tool that can automate the extraction of key fields from unstructured documents. Researchers have used different approaches for extracting key fields, but the lack of annotated and highquality datasets is the biggest challenge. Existing work in this area has used standard and custom datasets for extracting key fields from unstructured documents. Still, the existing datasets face some serious challenges, such as poor-quality images, domain-related datasets, and a lack of data validation approaches to evaluate data quality. This work highlights the detailed process flow for endto-end key fields extraction from unstructured documents. This work presents a high-quality, multi-layout unstructured invoice documents dataset assessed with a statistical data validation technique. The proposed multi-layout unstructured invoice documents dataset is highly diverse in invoice layouts to generalize key field extraction tasks for unstructured documents. The proposed multilayout unstructured invoice documents dataset is evaluated with various feature extraction techniques such as Glove, Word2Vec, FastText, and AI approaches such as BiLSTM and BiLSTM-CRF. We also present the comparative analysis of feature extraction techniques and AI approaches on the proposed multi-layout unstructured invoice document dataset. We attained the best results with BiLSTM-CRF model. INDEX TERMS Artificial Intelligence (AI), information extraction, key field extraction, Named Entity Recognition (NER), template-free invoice processing, unstructured data.

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GeoDeepShovel: A platform for building scientific database from geoscience literature withAIassistance

Shao

Jia

et al. 2023

Geoscience Data Journal

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With the rapid development of big data science, the research paradigm in the field of geosciences has also begun to shift to big data‐driven scientific discovery. Researchers need to read a huge amount of literature to locate, extract and aggregate relevant results and data that are published and stored in PDF format for building a scientific database to support the big data‐driven discovery. In this paper, based on the findings of a study about how geoscientists annotate literature and extract and aggregate data, we proposed GeoDeepShovel, a publicly available AI‐assisted data extraction system to support their needs. GeoDeepShovel leverages state‐of‐the‐art neural network models to support researcher(s) easily and accurately annotate papers (in the PDF format) and extract data from tables, figures, maps, etc., in a human–AI collaboration manner. As a part of the Deep‐Time Digital Earth (DDE) program, GeoDeepShovel has been deployed for 8 months, and there are already 400 users from 44 geoscience research teams within the DDE program using it to construct scientific databases on a daily basis, and more than 240 projects and 50,000 documents have been processed for building scientific databases.

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DocBank: A Benchmark Dataset for Document Layout Analysis

Cited by 144 publications

References 38 publications

A Survey of Graphical Page Object Detection with Deep Neural Networks

A Survey of Graphical Page Object Detection with Deep Neural Networks

Multi-Layout Unstructured Invoice Documents Dataset: A Dataset for Template-Free Invoice Processing and Its Evaluation Using AI Approaches

GeoDeepShovel: A platform for building scientific database from geoscience literature withAIassistance

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