Table Extraction from Document Images using Fixed Point Model

Bansal, Anukriti; Harit, Gaurav; Roy, Sumantra Dutta

doi:10.1145/2683483.2683550

Cited by 20 publications

(3 citation statements)

References 30 publications

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“…The system shows a 75% overall detection rate which was not very promising. Bansal et al [6] presented a learningbased framework which identifies tables from scanned document images. They proposed a scheme for analyzing and labeling different document elements, their contexts, and finally to define and understand the table boundaries from the context informations.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Robust Detection of Tables in Documents Using Scores from Table Cell Cores

et al. 2022

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Table detection is an essential step in many document analysis systems. Tabular data are a pivotal form of information representation that can organize data in a conventional structure for comfortable and quick information retrieval and comparison. Detection of table structures in PDF files or images is a challenging task because of the variability of table layouts, and sometimes the tabular structures’ similarities with non-tabular elements like charts, plots, etc. In this work, we have presented a table detection method using a geometric analysis of the table cell cores that represents the table cell texts. The proposed method works by analyzing the text gap information, and hence it can detect the table cell cores, irrespective of the presence of the table boundary lines and cell-separating rule-lines. Experimentations have been done on various document images of complex structures from well-known datasets. The detection accuracies obtained by us corroborate the usefulness of the proposed method.

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Section: Related Work and Motivationmentioning

confidence: 99%

Robust Detection of Tables in Documents Using Scores from Table Cell Cores

et al. 2022

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“…They identify a set of features that can be used to segregated headers from tabular data and build a classifier to detect table headers. In [2], researchers design learning-based framework to identify tables, it is a structured labeling problem, which learns the layout of the document and labels its various entities as table header, table trailer, table cell and non-table region. They develop features which encode the foreground block characteristics and the contextual information.…”

Section: Introductionmentioning

confidence: 99%

Table Detection from Document Image using Vertical Arrangement of Text Blocks

Tran¹,

Tran²,

Oh³

et al. 2015

International Journal of Contents

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Table detection is a challenging problem and plays an important role in document layout analysis. In this paper, we propose an effective method to identify the table region from document images. First, the regions of interest (ROIs) are recognized as the table candidates. In each ROI, we locate text components and extract text blocks. After that, we check all text blocks to determine if they are arranged horizontally or vertically and compare the height of each text block with the average height. If the text blocks satisfy a series of rules, the ROI is regarded as a table. Experiments on the ICDAR 2013 dataset show that the results obtained are very encouraging. This proves the effectiveness and superiority of our proposed method.

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“…use an SVM model to classify the attributes of each line to determine whether the line belongs to the table Barlas et al (2014). use an MLP model to classify the connected component in the document and determined whether it belongs to tables Bansal et al (2014). use the leptonica library Bloomberg (1991) to segment the document, and then construct features containing surrounding information for each region.…”

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confidence: 99%

Document AI: Benchmarks, Models and Applications

Cui¹,

Xu²,

Lv³

et al. 2021

Preprint

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Document AI, or Document Intelligence, is a relatively new research topic that refers to the techniques for automatically reading, understanding, and analyzing business documents. It is an important research direction for natural language processing and computer vision. In recent years, the popularity of deep learning technology has greatly advanced the development of Document AI, such as document layout analysis, visual information extraction, document visual question answering, document image classification, etc. This paper briefly reviews some of the representative models, tasks, and benchmark datasets. Furthermore, we also introduce early-stage heuristic rule-based document analysis, statistical machine learning algorithms, and deep learning approaches especially pre-training methods. Finally, we look into future directions for Document AI research.

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Table Extraction from Document Images using Fixed Point Model

Cited by 20 publications

References 30 publications

Robust Detection of Tables in Documents Using Scores from Table Cell Cores

Robust Detection of Tables in Documents Using Scores from Table Cell Cores

Table Detection from Document Image using Vertical Arrangement of Text Blocks

Document AI: Benchmarks, Models and Applications

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