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  • Off-line handwritten Thai name recognition for student identification in an automated assessment system

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    Author(s)
    Suwanwiwat, Hemmaphan
    Nguyen, Vu
    Blumenstein, Michael
    Pal, Umapada
    Griffith University Author(s)
    Blumenstein, Michael M.
    Suwanwiwat, Hemmaphan
    Nguyen, Vu
    Year published
    2014
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    Abstract
    In the field of pattern recognition, off-line handwriting recognition is one of the most intensive areas of study. This paper proposes an automatic off-line Thai language student name identification system which was built as a part of a completed off-line automated assessment system. There is limited work undertaken in developing off-line automatic assessment systems using handwriting recognition. To the authors' knowledge, none of the work on the proposed system has been performed on the Thai language. In addition the proposed system recognises each Thai name by using an approach for whole word recognition, which is different ...
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    In the field of pattern recognition, off-line handwriting recognition is one of the most intensive areas of study. This paper proposes an automatic off-line Thai language student name identification system which was built as a part of a completed off-line automated assessment system. There is limited work undertaken in developing off-line automatic assessment systems using handwriting recognition. To the authors' knowledge, none of the work on the proposed system has been performed on the Thai language. In addition the proposed system recognises each Thai name by using an approach for whole word recognition, which is different from the work found in the literature as most perform character-based recognition. In this proposed system, the Gaussian Grid Feature (GGF) and the Modified Direction Feature (MDF) extraction techniques are investigated on upper and lower contours, loops from full word contour images of each name sample, and artificial neural networks and support vector machine are used as classifiers. The encouraging recognition rates for both feature extraction techniques were achieved when applied on loop, upper and lower contour images (99.27% accuracy rate was achieved using MDF on artificial neural networks and 99.27% using GGF with a support vector machine classifier).
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    Conference Title
    Neural Networks (IJCNN), 2014 International Joint Conference on
    Publisher URI
    http://www.ieee-wcci2014.org/
    DOI
    https://doi.org/10.1109/IJCNN.2014.6889657
    Copyright Statement
    © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Image Processing
    Publication URI
    http://hdl.handle.net/10072/66727
    Collection
    • Conference outputs

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