This HTML5 document contains 31 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
n18doi:10.1007/
dctermshttp://purl.org/dc/terms/
n2https://kar.kent.ac.uk/id/eprint/
wdrshttp://www.w3.org/2007/05/powder-s#
dchttp://purl.org/dc/elements/1.1/
n19http://purl.org/ontology/bibo/status/
n12https://kar.kent.ac.uk/id/subject/
rdfshttp://www.w3.org/2000/01/rdf-schema#
n21https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/
n9https://kar.kent.ac.uk/91696/
n6http://eprints.org/ontology/
n10https://kar.kent.ac.uk/id/event/
bibohttp://purl.org/ontology/bibo/
n22https://kar.kent.ac.uk/id/publication/
n13https://kar.kent.ac.uk/id/org/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n11https://kar.kent.ac.uk/id/eprint/91696#
owlhttp://www.w3.org/2002/07/owl#
n5https://kar.kent.ac.uk/id/
n7https://kar.kent.ac.uk/id/document/
xsdhhttp://www.w3.org/2001/XMLSchema#
n16https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/
n14https://kar.kent.ac.uk/id/person/

Statements

Subject Item
n2:91696
rdf:type
bibo:AcademicArticle bibo:Article n6:ConferenceItemEPrint n6:EPrint
rdfs:seeAlso
n9:
owl:sameAs
n18:978-3-030-91100-3_16
n6:hasAccepted
n7:3255203
n6:hasDocument
n7:3255203 n7:3255208 n7:3255308 n7:3255309 n7:3255310 n7:3255311
dc:hasVersion
n7:3255203
dcterms:title
Automatic Information Extraction from Electronic Documents using Machine Learning
wdrs:describedby
n16:export_kar_RDFN3.n3 n21:export_kar_RDFN3.n3
dcterms:date
2021-12-06
dcterms:creator
n14:ext-f.e.b.otero@kent.ac.uk n14:ext-n.kamaleson@kent.ac.uk n14:ext-d.f.chu@kent.ac.uk
bibo:status
n19:peerReviewed n19:published
dcterms:publisher
n13:ext-1c5ddec173ca8cdfba8b274309638579
bibo: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.
dcterms:isPartOf
n5:repository n22:ext-03029743
dcterms:subject
n12:Q335
bibo:authorList
n11:authors
bibo:presentedAt
n10:ext-c588046f5d72c7f4f4b3cd443d2a9840
bibo:volume
13101