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dc.contributor.authorSuwanwiwat, Hemmaphan
dc.contributor.authorBlumenstein, Michael
dc.contributor.authorPal, Umapada
dc.date.accessioned2017-06-21T01:41:51Z
dc.date.available2017-06-21T01:41:51Z
dc.date.issued2015
dc.identifier.doi10.1109/ICDAR.2015.7333834
dc.identifier.urihttp://hdl.handle.net/10072/340496
dc.description.abstractThere are only a few studies undertaken in developing automatic assessment systems using handwriting recognition, even though a successful system would undoubtedly benefit the education system as schools and universities in many countries still employ paper-based examinations. To the best of the authors' knowledge, there is no existing work on an automatic off-line short answer assessment system comprising a student identification component. Hence in this paper, the authors propose a system towards this, where a new feature extraction technique called the Enhanced Water Reservoir, Loop and Gaussian Grid Feature, as well as other enhanced feature extraction techniques were utilised. Artificial Neural Networks and Support Vector Machines were employed as the classifiers; they were used for the investigation, and a comparison of the recognition and accuracy rates of the proposed systems, as well as the feature extraction techniques, was undertaken. The proposed assessment system achieved a recognition rate of 87.12% with 91.12% assessment accuracy, and the student identification component obtained a recognition rate of 99.52% with a 100% identification accuracy rate.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States of America
dc.relation.ispartofconferencenameICDAR 2015
dc.relation.ispartofconferencetitleICDAR 2015 13th IAPR International Conference on Document Analysis and Recognition Conference Proceedings
dc.relation.ispartofdatefrom2015-08-23
dc.relation.ispartofdateto2015-08-26
dc.relation.ispartoflocationNancy, France (relocated from Tunis, Tunisia)
dc.relation.ispartofedition1st
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.subject.fieldofresearchcode080199
dc.titleA complete automatic short answer assessment system with student identification
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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