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    • Graduate School - MU Theses and Dissertations (MU)
    • Theses and Dissertations (MU)
    • Theses (MU)
    • 2016 Theses (MU)
    • 2016 MU theses - Access restricted to MU
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    Convolutional Neural Network architectures for digit recognition system

    Al-Wzwazy, Haider A.
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    [PDF] research.pdf (2.803Mb)
    [PDF] short.pdf (106.0Kb)
    Date
    2016
    Format
    Thesis
    Metadata
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    Abstract
    [ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Rapidly evolving of Convolutional Neural Networks (CNNs) appeals and endeavors us to explore and discover various CNN model's robustness leading to establishing more effective and efficient models achieved state-of-the-art results on challenging datasets including novel benchmark used in this work. Comparing with most contemporary works, our models outperform the accuracy demonstrated by all existing models. Enhancement performed during this work is not only included model performance but also compromise other vital deep learning aspects such as speed and efficiency. Moreover, a new challenging Arabic handwriting digit dataset is also introduced in this work. We create novel dataset because recently handwritten digit recognition becomes vital scope and it is appealing many researchers. Also the lacking research of using Arabic digits endeavors us to dig deeper by creating our challenge Arabic Handwritten Digits which consists of more than 45,000 samples.
    URI
    https://doi.org/10.32469/10355/56381
    https://hdl.handle.net/10355/56381
    Degree
    M.S.
    Thesis Department
    Computer engineering (MU)
    Rights
    Access to files is limited to the University of Missouri--Columbia.
    Collections
    • 2016 MU theses - Access restricted to MU
    • Electrical Engineering and Computer Science electronic theses and dissertations (MU)

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