Threshold center-symmetric local binary convolutional neural networks for bilingual handwritten digit recognition
Arabic and English handwritten digit recognition is a challenging problem because the writing style differs from one writer to another. In middle east countries, many official forms are prepared to be written using either Arabic or English languages. However, some people fill the form using both...
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2022
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/8412/1/24p%20EBRAHIM%20QASEM%20SALEH%20AL-WAJIH.pdf http://eprints.uthm.edu.my/8412/2/EBRAHIM%20QASEM%20SALEH%20AL-WAJIH%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8412/3/EBRAHIM%20QASEM%20SALEH%20AL-WAJIH%20WATERMARK.pdf |
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| Summary: | Arabic and English handwritten digit recognition is a challenging problem because
the writing style differs from one writer to another. In middle east countries, many
official forms are prepared to be written using either Arabic or English languages.
However, some people fill the form using both languages (Arabic and English),
which adds more challenges to recognize digits. Nowadays, deep learning
approaches are considered the hot trend of new research, including Convolutional
Neural Networks (CNN). CNN is used in many applications and modified to produce
other models such as Local Binary Convolutional Neural Networks (LBCNN).
LBCNN was created by fusing Local Binary Pattern (LBP) with CNN by
reformulating LBP as a convolution layer called Local Binary Convolution (LBC).
However, LBCNN suffers from the random assign 1, 0, or -1 to LBC weights,
making LBCNN less robust. Nevertheless, using another LBP-based technique such
as Center-Symmetric Local Binary Patterns (CS-LBP) can address such issues. In
this thesis, a new model based on CS-LBP is proposed called Center-Symmetric
Local Binary Convolutional Neural Networks (CS-LBCNN) that addresses the issues
of LBCNN. Further, an enhanced version of CS-LBCNN is proposed called
Threshold Center-Symmetric Local Binary Convolutional Neural Networks (TCSLBCNN)
that addresses another issue related to the zero-thresholding function. The
proposed models are compared against state-of-the-art techniques that used the
MNIST and MADBase as a bilingual dataset. The proposed TCS-LBCNN model
proves its ability to give a more accurate and significant classification rate than the
existing LBCNN models. For the bilingual dataset, the TCS-LBCNN enhances the
performance of LBCNN and CS-LBCNN, in terms of accuracy, by 0.15% and
0.03%, respectively. In addition, the comparison shows that the accuracy acquired by
TCS-LBCNN is the second-highest using the MNIST and MADBase datasets. |
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