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    •   BracU IR
    • School of Data and Sciences (SDS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    An Analysis on Bengali handwritten conjunct character recognition and prediction

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    17101036, 17101019, 17101350_CSE.pdf (4.444Mb)
    Date
    2021-01
    Publisher
    Brac University
    Author
    Munawar, Maazin
    Roy, Yagghaseni Saha
    Hussain, Mohammed Mudabbir
    Metadata
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    URI
    http://hdl.handle.net/10361/15514
    Abstract
    In the very active field of handwriting recognition, a lot of research can be found in the detection of the handwriting of various languages, especially English. However, for languages like Bengali, while they hold some success in handwritten character recognition, a big roadblock is Bengali conjunct characters or “Juktakkhor”. As Bengali conjunct characters are very complex, even today many institutions in Bangladesh still maintain documents as handwritten copies. In this paper, we will present a model that focuses on conjunct character recognition and conversion to textformat. OurproposedsystemwillbetrainedandtestedusingCNNmodelslike VGG19, ResNet-50, GoogleNet, LSTM, ShuffleNet etc. The results generated from preliminary analysis yield that ShuffleNet gives the most accurate results with an accuracy of 91.2% followed by GoogleNet with 73.3%.
    Keywords
    Conjunct Characters; Handwritten; Neural Networks; Bengali; Segmentation; ResNet-50; ShuffleNet; LSTM; GoogleNet
     
    LC Subject Headings
    Neural Networks
     
    Description
    This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
     
    Cataloged from PDF version of thesis.
     
    Includes bibliographical references (pages 29-31).
    Department
    Department of Computer Science and Engineering, Brac University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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