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dc.contributor.authorSuwanwiwat, Hemmaphan
dc.contributor.authorBlumenstein, Michael
dc.contributor.authorPal, Umapada
dc.contributor.editorAmir Hussain
dc.date.accessioned2018-03-15T03:36:52Z
dc.date.available2018-03-15T03:36:52Z
dc.date.issued2015
dc.identifier.doi10.1109/IJCNN.2015.7280538
dc.identifier.urihttp://hdl.handle.net/10072/125371
dc.description.abstractOff-line automatic assessment systems can be an aid for teachers in the marking process. There has been no recent work in the development of off-line automatic assessment systems using handwriting recognition, even though such systems will clearly benefit the education sector. The reason is many schools and universities in many parts of the world still use paper-based examination. This research proposes the use of a newly developed feature extraction technique called the Modified Water Reservoir, Loop and Gaussian Grid Feature, as well as other feature extraction techniques. These techniques were investigated employing artificial neural networks and support vector machines as classifiers to develop an automatic assessment system for marking short answer questions. The system has high assessment accuracy (up to 94.75% for hand printed, 96.09% for cursive handwritten, and 95.71% for hand printed and cursive handwritten combined). The proposed system also includes assessment criteria to augment its accuracy.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States
dc.relation.ispartofconferencenameIJCNN 2015
dc.relation.ispartofconferencetitle2015 International Joint Conference on Neural Networks (IJCNN)
dc.relation.ispartofdatefrom2015-07-12
dc.relation.ispartofdateto2015-07-17
dc.relation.ispartoflocationKillarney, Ireland
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.subject.fieldofresearchcode080199
dc.titleShort Answer Question Examination using an Automatic Off-line Handwriting Recognition System and a Novel Combined Feature
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorBlumenstein, Michael M.
gro.griffith.authorSuwanwiwat, Hemmaphan


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    Contains papers delivered by Griffith authors at national and international conferences.

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