PubTables-1M: Towards comprehensive table extraction from unstructured documents

Smock, Brandon; Rohith, Pesala,; Abraham, Robin

doi:10.1109/cvpr52688.2022.00459

Cited by 54 publications

(11 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the flourish of transformer [43], several DETR [2]like TSR methods are proposed to extract boundary in object detection paradigm. In work [40], a set of table elements are detected by DETR to model the hierarchical structure. TRUST [9] has the similar spirit with SP-LERGE [41], where the features of row/column separators are extracted for a further vertex-based merging module to predict the linking relations between adjacent cells, which thus still suffers from boundary ambiguity problem.…”

Section: Candidate Components Extraction Separator Proposals Generati...mentioning

confidence: 99%

Neural Collaborative Graph Machines for Table Structure Recognition

Líu¹,

Li²,

Liu³

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

View full text Add to dashboard Cite

Recently, Table Structure Recognition (TSR) task, aiming at identifying table structure into machine readable formats, has received increasing interest in the community. While impressive success, most single table componentbased methods can not perform well on unregularized table cases distracted by not only complicated inner structure but also exterior capture distortion. In this paper, we raise it as Complex TSR problem, where the performance degeneration of existing methods is attributable to their inefficient component usage and redundant post-processing. To mitigate it, we shift our perspective from table component extraction towards the efficient multiple components leverage, which awaits further exploration in the field. Specifically, we propose a seminal method, termed GrabTab, equipped with newly proposed Component Deliberator. Thanks to its progressive deliberation mechanism, our GrabTab can flexibly accommodate to most complex tables with reasonable components selected but without complicated postprocessing involved. Quantitative experimental results on public benchmarks demonstrate that our method significantly outperforms the state-of-the-arts, especially under more challenging scenes.

show abstract

Section: Candidate Components Extraction Separator Proposals Generati...mentioning

confidence: 99%

Neural Collaborative Graph Machines for Table Structure Recognition

Líu¹,

Li²,

Liu³

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

View full text Add to dashboard Cite

show abstract

“…extraction as defined in [5] that adds layout information and encourages the development of end-to-end systems that can tackle multiple tasks at once;…”

Section: • We Define the Task Of Contextualized Table Extraction An E...mentioning

confidence: 99%

“…PubLayNet is a collection of 358, 353 PDF pages with five types of regions annotated (title, text, list, table, image) [4]. PubTables-1M [5] is a collection of 947, 642 fully annotated tables, including information for table detection, recognition, and functional analysis (such as identifying column headers, projected rows, and table cells). The datasets are built to address different tasks, as summarized in Table 1.…”

Section: Subset Of Publaynet and Pubtables-1mmentioning

confidence: 99%

“…Moreover, collections of scientific papers such as arXiv and PubMed opened to the possibility of accessing a large number of documents along with their structural information represented in standard formats such as L A T E X and XML. That is why scientific literature parsing and scientific table analysis rapidly became one of the most prominent areas of research in DAR: large datasets have been released [4,5], allowing the community to develop deep learning models. Unfortunately, as we will describe in the next sections, these datasets come with partial information that forces the experimentation of layout analysis and table extraction separately.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

CTE: A Dataset for Contextualized Table Extraction

Gemelli¹,

Vivoli²,

Marinai³

2023

Preprint

View full text Add to dashboard Cite

Relevant information in documents is often summarized in tables, helping the reader to identify useful facts. Most benchmark datasets support either document layout analysis or table understanding, but lack in providing data to apply both tasks in a unified way. We define the task of Contextualized Table Extraction (CTE), which aims to extract and define the structure of tables considering the textual context of the document. The dataset comprises 75k fully annotated pages of scientific papers, including more than 35k tables. Data are gathered from PubMed Central, merging the information provided by annotations in the PubTables-1M and PubLayNet datasets. The dataset can support CTE and adds new classes to the original ones. The generated annotations can be used to develop end-to-end pipelines for various tasks, including document layout analysis, table detection, structure recognition, and functional analysis. We formally define CTE and evaluation metrics, showing which subtasks can be tackled, describing advantages, limitations, and future works of this collection of data. Annotations and code will be accessible at https://github.com/AILab-UniFI/cte-dataset.

show abstract

“…Recent advances with the transformer architecture [35] have led to improvements within document image analysis fields, including table understanding. The TableTransformer [32] is a model for detecting tables and extracting table structure from images and PDF documents. SegFormer [36] and later DocSegTr [8] are other attempts to use the transformer architecture for general document and image segmentation.…”

Section: Document Image Analysismentioning

confidence: 99%

Text Line Detection in Historical Index Tables: Evaluations on a New French PArish REcord Survey Dataset (PARES)

Bernard,

Wall,

Boillet

et al. 2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

In this paper, we address the challenge of document image analysis for historical index table documents with handwritten records. Demographic studies can gain insight from the use of automatic document analysis in such documents through the study of population movements. To evaluate the efficacy of automatic layout analysis tools, we release the PARES dataset [6], which contains 250 labeled index table images originating from French archives. Also, we run state-of-the-art algorithms (U-FCN, R-CNN and Transformers) in order to detect the lines within index tables, a common prerequisite for handwritten text recognition (HTR). Our results indicate that text line extraction works well with the U-FCN model, while also indicating that Transformer architectures show promise for accurate text line detection in such historical documents with great efficiency. This is a encouraging step towards a Transformer-based architecture for both layout and content detection. This process and dataset represent a first step to automatically analyze handwritten and historical index tables. In addition to this paper and the PARES [6] dataset of historical index tables of 250 images, we release segmentation masks, the code we used to train and test the models, and the models themselves.

show abstract

PubTables-1M: Towards comprehensive table extraction from unstructured documents

Cited by 54 publications

References 15 publications

Neural Collaborative Graph Machines for Table Structure Recognition

Neural Collaborative Graph Machines for Table Structure Recognition

CTE: A Dataset for Contextualized Table Extraction

Text Line Detection in Historical Index Tables: Evaluations on a New French PArish REcord Survey Dataset (PARES)

Contact Info

Product

Resources

About