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A Complete Automatic Short Answer Assessment System With Student Identification 13

A Complete Automatic Short Answer Assessment System With Stu...

引用

13th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Suwanwiwat, Hemmaphan Blumenstein, Michael Pal, Umapada Griffith Univ Sch Informat & Commun Technol Nathan Qld 4111 Australia Indian Stat Inst Comp Vis & Pattern Recognit Unit Kolkata W Bengal India

ISBN: (纸本)9781479918058

There 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.

关键词： Modified Direction feature gaussian grid feature Water Reservoir feature Enhanced Water Reservoir loop and gaussian grid feature Off-line Automatic Assessment System Student Identification System

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Short Answer Question Examination using an Automatic Off-line Handwriting Recognition System and a Novel Combined feature

Short Answer Question Examination using an Automatic Off-lin...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Suwanwiwat, Hemmaphan Blumenstein, Michael Pal, Umapada Griffith Univ Sch Informat & Commun Technol Nathan Qld 4111 Australia Indian Stat Inst Comp Vis & Pattern Recognit Unit Delhi India

ISBN: (纸本)9781479919598

Off-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.

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Short Answer Question Examination using an Automatic Off-line Handwriting Recognition System and a Novel Combined feature

Short Answer Question Examination using an Automatic Off-lin...

引用

International Joint Conference on Neural Networks

作者： Hemmaphan Suwanwiwat Michael Blumenstein Umapada Pal School of Information and Communication Technology Griffith University Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

ISBN: (纸本)9781479919611

Off-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.

关键词： Handwriting recognition Off-line Automatic Assessment System Modified direction feature gaussian grid feature Water Reservoir feature Modified Water Reservoir loop and gaussian grid feature handwriting recognition AUTOMATIC feature extraction OFF LINE Hand Answer Classifiers Support Vector Network

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