Table detection in document images using header and trailer patterns
16 Dec 2012-pp 62
TL;DR: This paper presents a new approach to detect tabular structures present in document images and in low resolution video images based on identifying the unique table start pattern and table trailer pattern and formulated perceptual attributes to characterize the patterns.
Abstract: This paper presents a new approach to detect tabular structures present in document images and in low resolution video images. The algorithm for table detection is based on identifying the unique table start pattern and table trailer pattern. We have formulated perceptual attributes to characterize the patterns. The performance of our table detection system is tested on a set of document images picked from UW-III (University of Washington) dataset, UNLV dataset, video images of NPTEL videos, and our own dataset. Our approach demonstrates improved detection for different types of table layouts, with or without ruling lines. We have obtained correct table localization on pages with multiple tables aligned side-by-side.
Citations
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01 Nov 2017
TL;DR: The proposed method works with high precision on document images with varying layouts that include documents, research papers, and magazines and beats Tesseract's state of the art table detection system by a significant margin.
Abstract: Table detection is a crucial step in many document analysis applications as tables are used for presenting essential information to the reader in a structured manner. It is a hard problem due to varying layouts and encodings of the tables. Researchers have proposed numerous techniques for table detection based on layout analysis of documents. Most of these techniques fail to generalize because they rely on hand engineered features which are not robust to layout variations. In this paper, we have presented a deep learning based method for table detection. In the proposed method, document images are first pre-processed. These images are then fed to a Region Proposal Network followed by a fully connected neural network for table detection. The proposed method works with high precision on document images with varying layouts that include documents, research papers, and magazines. We have done our evaluations on publicly available UNLV dataset where it beats Tesseract's state of the art table detection system by a significant margin.
159 citations
Cites methods from "Table detection in document images ..."
...[12] proposed a technique for table detection based on the identification of unique table start and trailer pattern....
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23 Aug 2020
TL;DR: In this paper, the authors focus on tables that have complex structures, dense content, and varying layouts with no dependency on meta-features and/or optical character recognition (OCR) models.
Abstract: Tables are information-rich structured objects in document images. While significant work has been done in localizing tables as graphic objects in document images, only limited attempts exist on table structure recognition. Most existing literature on structure recognition depends on extraction of meta-features from the pdf document or on the optical character recognition (ocr) models to extract low-level layout features from the image. However, these methods fail to generalize well because of the absence of meta-features or errors made by the ocr when there is a significant variance in table layouts and text organization. In our work, we focus on tables that have complex structures, dense content, and varying layouts with no dependency on meta-features and/or ocr.
53 citations
22 Dec 2014
TL;DR: In this paper, the authors proposed an algorithm using local thresholds for word space and line height to locate and extract all categories of tables from scanned document images, which has an overall accuracy of about 75%.
Abstract: Pool of knowledge available to the mankind depends on the source of learning resources, which can vary from ancient printed documents to present electronic material. The rapid conversion of material available in traditional libraries to digital form needs a significant amount of work if we are to maintain the format and the look of the electronic documents as same as their printed counterparts. Most of the printed documents contain not only characters and its formatting but also some associated non text objects such as tables, charts and graphical objects. It is challenging to detect them and to concentrate on the format preservation of the contents while reproducing them. To address this issue, we propose an algorithm using local thresholds for word space and line height to locate and extract all categories of tables from scanned document images. From the experiments performed on 298 documents, we conclude that our algorithm has an overall accuracy of about 75% in detecting tables from the scanned document images. Since the algorithm does not completely depend on rule lines, it can detect all categories of tables in a range of scanned documents with different font types, styles and sizes to extract their formatting features. Moreover, the algorithm can be applied to locate tables in multi column layouts with small modification in layout analysis. Treating tables with their existing formatting features will tremendously help the reproducing of printed documents for reprinting and updating purposes.
27 citations
14 Dec 2014
TL;DR: A novel learning-based framework to identify tables from scanned document images as 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 is presented.
Abstract: The paper presents a novel learning-based framework to identify tables from scanned document images. The approach is designed as 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. We develop features which encode the foreground block characteristics and the contextual information. These features are provided to a fixed point model which learns the inter-relationship between the blocks. The fixed point model attains a contraction mapping and provides a unique label to each block. We compare the results with Condition Random Fields(CRFs). Unlike CRFs, the fixed point model captures the context information in terms of the neighbourhood layout more efficiently. Experiments on the images picked from UW-III (University of Washington) dataset, UNLV dataset and our dataset consisting of document images with multicolumn page layout, show the applicability of our algorithm in layout analysis and table detection.
23 citations
Cites background from "Table detection in document images ..."
...[12] G. Harit and A. Bansal....
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...Harit and Bansal [12] detected tables in document images by searching for table-header and trailer patterns....
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...Various approaches for table detection and layout analysis can be categorized as machine-learning based [33] [24] [15] [35] [6] [9] [5] [14], rule-based [12] [21] and model/template based [25] [30]....
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...[1] A. Bansal, S. Chaudhury, S. Dutta Roy, and J. B. Srivastava....
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TL;DR: A robust system for detecting table regions in the document image by using a new shape which is called Random Rotation Bounding Box which can detect most kinds of tables with high precision even when it is skewed.
15 citations
References
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TL;DR: This presentation clarifies both the decisions made by a table recognizer and the assumptions and inferencing techniques that underlie these decisions.
Abstract: Table characteristics vary widely. Consequently, a great variety of computational approaches have been applied to table recognition. In this survey, the table recognition literature is presented as an interaction of table models, observations, transformations, and inferences. A table model defines the physical and logical structure of tables; the model is used to detect tables and to analyze and decompose the detected tables. Observations perform feature measurements and data lookup, transformations alter or restructure data, and inferences generate and test hypotheses. This presentation clarifies both the decisions made by a table recognizer and the assumptions and inferencing techniques that underlie these decisions.
334 citations
18 Aug 1997
TL;DR: The architecture of a system for reading machine-printed documents in known predefined tabular-data layout styles, and algorithms for identifying and segmenting records with known layout, and integration of these algorithms with a graphical user interface (GUI) for defining new layouts are described.
Abstract: We describe the architecture of a system for reading machine-printed documents in known predefined tabular-data layout styles. In these tables, textual data are presented in record lines made up of fixed-width fields. Tables often do not rely on line-art (ruled lines) to delimit fields, and in this way differ crucially from fixed forms. Our system performs these steps: copes with multiple tables per page; identifies records within tables; segments records into fields; and recognizes characters within fields, constrained by field-specific contextual knowledge. Obstacles to good performance on tables include small print, tight line-spacing, poor-quality text (such as photocopies), and line-art or background patterns that touch the text. Precise skew-correction and pitch-estimation, and high-performance OCR using neural nets proved crucial in overcoming these obstacles. The most significant technical advances in this work appear to be algorithms for identifying and segmenting records with known layout, and integration of these algorithms with a graphical user interface (GUI) for defining new layouts. This GUI has been ergonomically designed to make efficient and intuitive use of exemplary images, so that the skill and manual effort required to retarget the system to new table layouts are held to a minimum. The system has been applied in this way to more than 400 distinct tabular layouts. During the last three years the system has read over fifty million records with high accuracy.
142 citations
22 Aug 2005
TL;DR: The efficiency of the proposed method is demonstrated by using a performance evaluation scheme which considers a great variety of documents such as forms, newspapers/magazines, scientific journals, tickets/bank cheques, certificates and handwritten documents.
Abstract: In this paper, we propose a novel technique for automatic table detection in document images. Lines and tables are among the most frequent graphic, non-textual entities in documents and their detection is directly related to the OCR performance as well as to the document layout description. We propose a workflow for table detection that comprises three distinct steps: (i) image pre-processing; (ii) horizontal and vertical line detection and (iii) table detection. The efficiency of the proposed method is demonstrated by using a performance evaluation scheme which considers a great variety of documents such as forms, newspapers/magazines, scientific journals, tickets/bank cheques, certificates and handwritten documents.
125 citations
"Table detection in document images ..." refers background or methods in this paper
...The performance of our table detection system is tested on a set of document images picked from UW-III (University of Washington) dataset, UNLV dataset, video images of NPTEL videos, and our own dataset....
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...It is desirable that the tabular structures in the document be identi.ed before OCR so that the layout and inter-relations between the table elements can be preserved for subsequent analysis....
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TL;DR: An efficient approach to identify tabular structures within either electronic or paper documents by taking word bounding box information as input, and outputs the corresponding logical text block units through the T-Recs system.
Abstract: This paper presents an efficient approach to identify tabular structures within either electronic or paper documents. The resulting T-Recs system takes word bounding box information as input, and outputs the corresponding logical text block units. Starting with an arbitrary word as block seed the algorithm recursively expands this block to all words that interleave with their vertical neighbors. Since even smallest gaps of table columns prevent their words from mutual interleaving, this initial segmentation is able to identify and isolate such columns. In order to deal with some inherent segmentation errors caused by isolated lines, overhanging words, or cells spawning more than one column, a series of postprocessing steps is added. These steps benefit form a very simple distinction between type 1 and type 2 blocks: type 1 blocks are those of at most one word per line, all others are of type 2. This distinction allows the selective application of heuristics to each group of blocks. The conjoint decomposition of column blocks into subsets of table cells leads to the final block segmentation of a homogeneous abstraction level. These segments serve the final layout analysis which identifies table environments and cells that are stretching over several rows and/or columns.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
123 citations
"Table detection in document images ..." refers methods in this paper
...A model .le for the table indicates the graphical features (e.g. lines and white spaces) to be used for analyzing the table....
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...Our approach demonstrates improved detection for di.erent types of table layouts, with or without ruling lines....
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09 Jun 2010
TL;DR: Evaluation of the algorithm on document images from publicly available UNLV dataset shows competitive performance in comparison to the table detection module of a commercial OCR system.
Abstract: Detecting tables in document images is important since not only do tables contain important information, but also most of the layout analysis methods fail in the presence of tables in the document image. Existing approaches for table detection mainly focus on detecting tables in single columns of text and do not work reliably on documents with varying layouts. This paper presents a practical algorithm for table detection that works with a high accuracy on documents with varying layouts (company reports, newspaper articles, magazine pages, ...). An open source implementation of the algorithm is provided as part of the Tesseract OCR engine. Evaluation of the algorithm on document images from publicly available UNLV dataset shows competitive performance in comparison to the table detection module of a commercial OCR system.
122 citations