Artificial Neural Networks-Driven High Precision Tabular Information Extraction from Datasheets
type: CreativeWork
description: Global organizations have adopted Industry 4.0 practices to stay viable through the information shared through billions of digital documents. The information in such documents is vital to the daily functioning of such organizations. Most critical information is laid out in tabular format in order to provide the information in a concise manner. Extracting this critical data and providing access to the latest information can help institutions to make evidence based and data driven decisions. Assembling such data for analysis can further enable organizations to automate certain processes such as manufacturing. A generalized solution for table text extraction would have to handle the variations in the page content and table layouts in order to accurately extract the text. We hypothesize that a table text extraction pipeline can extract this data in three stages. The first stage would involve identifying the images that contain tables and detecting the table region. The second stage would consider the detected table region and detect the rows and columns of the table. The last stage would involve extracting the text from the cell locations generated by the intersecting lines of the detected rows and columns. For first stage of the pipeline, we propose TableDet: a deep learning (artificial neural network) based methodology to solve table detection and table image classification in datasheet (document) images in a single inference. TableDet utilizes a Cascade R-CNN architecture with Complete IOU (CIOU) loss at each box head and a deformable convolution backbone to capture the variations of tables that appear at multiple scales and orientations. It also detects text and figures to enhance its table detection performance. We demonstrate the effectiveness of training TableDet with a dual-step transfer learning process and fine-tuning it with Table Aware Cutout (TAC) augmented images. TableDet achieves the highest F1 score for table detection against state-of-the-art solutions…
abstract: Global organizations have adopted Industry 4.0 practices to stay viable through the information shared through billions of digital documents. The information in such documents is vital to the daily functioning of such organizations. Most critical information is laid out in tabular format in order to provide the information in a concise manner. Extracting this critical data and providing access to the latest information can help institutions to make evidence based and data driven decisions. Assembling such data for analysis can further enable organizations to automate certain processes such as manufacturing. A generalized solution for table text extraction would have to handle the variations in the page content and table layouts in order to accurately extract the text. We hypothesize that a table text extraction pipeline can extract this data in three stages. The first stage would involve identifying the images that contain tables and detecting the table region. The second stage would consider the detected table region and detect the rows and columns of the table. The last stage would involve extracting the text from the cell locations generated by the intersecting lines of the detected rows and columns. For first stage of the pipeline, we propose TableDet: a deep learning (artificial neural network) based methodology to solve table detection and table image classification in datasheet (document) images in a single inference. TableDet utilizes a Cascade R-CNN architecture with Complete IOU (CIOU) loss at each box head and a deformable convolution backbone to capture the variations of tables that appear at multiple scales and orientations. It also detects text and figures to enhance its table detection performance. We demonstrate the effectiveness of training TableDet with a dual-step transfer learning process and fine-tuning it with Table Aware Cutout (TAC) augmented images. TableDet achieves the highest F1 score for table detection against state-of-the-art solutions…
author: Fernandes, Johan
language: en
keywords: Deep Learning, Information Extraction, Table Detection, Document Processing, Supply Chain Optimization, Image Processing
date published: 2022-03-11T14:27:33Z
date modified: 2026-07-24T03:39:42Z
provider: University of Ottawa
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Université d'Ottawa / University of Ottawa

PropertyValue
property ID: Handle
value: http://hdl.handle.net/10393/43374

PropertyValue
property ID: DOI
value: 10.20381/ruor-27591