About: Automatic Information Extraction from Electronic Documents using Machine Learning

Not logged in : Login

(Sponging disallowed)

Facets (new session)
Description
Metadata
Settings
- Rule:
- Inverse Functional Properties:
- "Same As":

About: Automatic Information Extraction from Electronic Documents using Machine Learning Goto Sponge NotDistinct Permalink

An Entity of Type : bibo:AcademicArticle, within Data Space : demo.openlinksw.com associated with source document(s)

Attributes	Values
type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #91696 Automatic Information Extraction from Electronic Documents using Machine Learning
sameAs	Automatic Information Extraction from Electronic Documents using Machine Learning
http://eprints.org/ontology/hasAccepted	Automatic Information Extraction from Electronic Documents using Machine Learning (PDF)
http://eprints.org/ontology/hasDocument	Automatic Information Extraction from Electronic Documents using Machine Learning (PDF) Automatic Information Extraction from Electronic Documents using Machine Learning (Other) Automatic Information Extraction from Electronic Documents using Machine Learning (Other) Automatic Information Extraction from Electronic Documents using Machine Learning (Other) Automatic Information Extraction from Electronic Documents using Machine Learning (Other) Automatic Information Extraction from Electronic Documents using Machine Learning (Other)
dc:hasVersion	Automatic Information Extraction from Electronic Documents using Machine Learning (PDF)
Title	Automatic Information Extraction from Electronic Documents using Machine Learning
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2021-12-06
Creator	Dominique Chu Fernando E.B. Otero Nishanthan Kamaleson
status	peer reviewed published
Publisher	Springer
abstract	The digital processing of electronic documents is widely exploited across many domains to improve the efficiency of information extraction. However, paper documents are still largely being used in practice. In order to process such documents, a manual procedure is used to inspect them and extract the values of interest. As this task is monotonous and time consuming, it is prone to introduce human errors during the process. In this paper, we present an efficient and robust system that automates the aforementioned task by using a combination of machine learning techniques: optical character recognition, object detection and image processing techniques. This not only speeds up the process but also improves the accuracy of extracted information compared to a manual procedure.
Is Part Of	Financial Cryptography and Data Security https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/91696#authors
presented at	41st SGAI International Conference on Artificial Intelligence
volume	13101
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #91696 Automatic Information Extraction from Electronic Documents using Machine Learning

Faceted Search & Find service v1.17_git144 as of Jul 26 2024

Alternative Linked Data Documents: iSPARQL | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 08.03.3331 as of Aug 25 2024, on Linux (x86_64-ubuntu_noble-linux-glibc2.38-64), Single-Server Edition (378 GB total memory, 45 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software