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.

Table Detection Using Deep Learning

Table detection is a crucial step in many document analysis applications as tables are used for presenting essential information to readers in a structured manner. It is still a challenging problem due to the variety of table structures and the complexity of document layout. This paper presents a hybrid method consisting of three fundamental steps to detect table zones: classification of the regions, detection of the tables that constitute intersecting horizontal and vertical lines, and identification of the tables made up by only parallel lines. Experiments on the UW-III dataset show that the obtained results are very promising.

Learning to detect tables in document images using line and text information

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

This paper proposes a method to extract table cells from document images. In standard cases, we can extract the cells using the ruling lines. But, there are also cases where the ruling lines are omitted. In such cases, the existing lines, if any, and the distribution of text blocks is the only information to decompose the table. In our method, the input image is divided into text and lines images. And then, we decompose the table from those two images separately. Finally, we analyze the cells obtained from the previous step and decide the final result of the table decomposition.

Cell decomposition for the table in document image based on analysis of texts and lines distribution

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In this paper, we present a hybrid method consisting of three main stages for detecting tables in document images. Based on table structure, our system separates table into two main categories, ruling line table and non-ruling line table. In the first stage, the text and non-text elements in document are classified by a heuristic filter. Then, the white space analysis is used to group the text elements into text lines, while ruling line table candidates are identified from non-text elements. In the second stage, based on the text lines, text and non-text elements, a hybrid method which consist of the alternative bottom-up and top-down approaches is implemented to find the table region candidates. In the final stage, these candidates are examined to get the table regions by analyzing text lines and spare lines. Experimental results with the document database from the ICDAR2013 table competition show that the proposed method works better than the previous ones.

Na Inseop

Papers

A hybrid method for table detection from document image

Tracking by Detection of Multiple Faces using SSD and CNN Features

Novel Graph Matching using Bayesian Inference and Image Retrieval

Color Adaptive Method for Vehicle Shadow Removal