PDF-TREX: An Approach for Recognizing and Extracting Tables from PDF Documents (original) (raw)

TAO: System for Table Detection and Extraction from PDF Documents

2016

Digital documents present knowledge in most areas of study, exchanging and communicating information in a portable way. To better use the knowledge embedded in an ever-growing information source, effective tools for automatic information extraction are needed. Tables are crucial information elements in documents of scientific nature. Most publications use tables to represent and report concrete findings of research. Current methods used to extract table data from PDF documents lack precision in detecting, extracting, and representing data from diverse layouts. We present the system TAble Organization (TAO) to automatically detect, extract and organize information from tables in PDF documents. TAO uses a processing, based on the k-nearest neighbor method and layout heuristics, to detect tables within a document and to extract table information. This system generates an enriched representation of the data extracted from tables in the PDF documents. TAO’s performance is comparable to o...

TabbyPDF: Web-Based System for PDF Table Extraction

Communications in Computer and Information Science, 2018

PDF is one of the most widespread ways to represent noneditable documents. Many of PDF documents are machine-readable but remain untagged. They have no tags for identifying layout items such as paragraphs, columns, or tables. One of the important challenges with these documents is how to extract tabular data from them. The paper presents a novel web-based system for extracting tables located in untagged PDF documents with a complex layout, for recovering their cell structures, and for exporting them into a tagged form (e.g. in CSV or HTML format). The system uses a heuristic-based approach to table detection and structure recognition. It mainly relies on recovering a human reading order of text, including document paragraphs and table cells. A prototype of the system was evaluated, using the methodology and dataset of "ICDAR 2013 Table Competition". The standard metric F-score is 93.64% for the structure recognition phase and 83.18% for the table extraction with automatic table detection. The results are comparable with the state-of-the-art academic solutions.

pdf2table: A Method to Extract Table Information from PDF Files

2005

Tables are a common structuring element in many documents, such as PDF files. To reuse such tables, appropriate methods need to be develop, which capture the structure and the content information. We have developed several heuristics which together recognize and decompose tables in PDF files and store the extracted data in a structured data format (XML) for easier reuse. Additionally, we implemented a prototype, which gives the user the ability of making adjustments on the extracted data. Our work shows that purely heuristic-based approaches can achieve good results, especially for lucid tables.

A methodology for evaluating algorithms for table understanding in PDF documents

2012

This paper presents a methodology for the evaluation of table understanding algorithms for PDF documents. The evaluation takes into account three major tasks: table detection, table structure recognition and functional analysis. We provide a general and flexible output model for each task along with corresponding evaluation metrics and methods. We also present a methodology for collecting and ground-truthing PDF documents based on consensusreaching principles and provide a publicly available groundtruthed dataset.

Automatic Table Recognition and Extraction from Heterogeneous Documents

Journal of Computer and Communications, 2015

This paper examines automatic recognition and extraction of tables from a large collection of heterogeneous documents. The heterogeneous documents are initially pre-processed and converted to HTML codes, after which an algorithm recognises the table portion of the documents. Hidden Markov Model (HMM) is then applied to the HTML code in order to extract the tables. The model was trained and tested with five hundred and twenty six self-generated tables (three hundred and twenty-one (321) tables for training and two hundred and five (205) tables for testing). Viterbi algorithm was implemented for the testing part. The system was evaluated in terms of accuracy, precision, recall and f-measure. The overall evaluation results show 88.8% accuracy, 96.8% precision, 91.7% recall and 88.8% F-measure revealing that the method is good at solving the problem of table extraction.

Detection, extraction and representation of tables

Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings., 2003

We are concerned with the extraction of tables from exchange format representations of very diverse composite documents. We put forward a flexible representation scheme for complex tables, based on a clear distinction between the physical layout of a table and its logical structure. Relying on this scheme, we develop a new method for the detection and the extraction of tables by an analysis of the graphic lines.

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

SN Computer Science

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...

Table detection in heterogeneous documents

Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, 2010

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.

A simple and effective table detection system from document images

International Journal on Document Analysis and Recognition, 2006

The requirement of detection and identification of tables from document images is crucial to any document image analysis and digital library system. In this paper we report a very simple but extremely powerful approach to detect tables present in document pages. The algorithm relies on the observation that the tables have distinct columns which implies that gaps between the fields are substantially larger than the gaps between the words in text lines. This deceptively simple observation has led to the design of a simple but powerful table detection system with low computation cost. Moreover, mathematical foundation of the approach is also established including formation of a regular expression for ease of implementation.

A Very Efficient Table Detection System from Document Images

The requirement of detection and identification of tables from document images is crucial to any document image analysis and digital library system. Here in this paper we report a very simple but extremely powerful approach to detect any table in any form that may be present in a document page. The algorithm rely on the observation that the tables has distinct columns whose physical implication is in the presence of substantially larger gaps between the fields than the gaps between the words in text lines. This deceptively simple observation has led to the design of a simple but powerful table detection system with a low computation cost and achieving an efficiency close to 100%. Moreover, mathematical foundation of the approach is also established including formation of a regular expression for ease of implementation.