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

    Author
    Suwanwiwat, Hemmaphan
    Blumenstein, Michael
    Pal, Umapada
    Year published
    2015
    Metadata
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    Abstract
    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 ...
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    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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    Conference Title
    2015 International Joint Conference on Neural Networks (IJCNN)
    DOI
    https://doi.org/10.1109/IJCNN.2015.7280538
    Subject
    Artificial Intelligence and Image Processing not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/125371
    Collection
    • Conference outputs

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