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EbrahimAlwajih · Jul 2, 2024 · 16deeff · 16deeff
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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>TCS-LBCNN</title>
+</head>
+<body>
+    <h1>TCS-LBCNN</h1>
+    <p><strong>Torch implementation of - Threshold Center-Symmetric Local Binary Convolutional Neural Networks for Bilingual Handwritten Digit Recognition</strong></p>
+    <p><a href="https://github.com/EbrahimAlwajih/TCSLBCNN-Pytorch-implementation">Pytorch Implementation</a></p>
+
+    <h2>Abstract</h2>
+    <p>
+        The writing style of the same writer varies from instance to instance in Arabic and English handwritten digit recognition, making handwritten digit recognition challenging. 
+        Currently, deep learning approaches are applied in many applications, including convolutional neural networks (CNNs) modified to produce other models, such as local binary convolutional neural networks (LBCNNs). 
+        An LBCNN is created by fusing a local binary pattern (LBP) with a CNN by reformulating the LBP as a convolution layer called a local binary convolution (LBC). 
+        However, LBCNNs suffer from the random assignment of 1, 0, or -1 to LBC weights, making LBCNNs less robust. 
+        Nevertheless, using another LBP-based technique, such as center-symmetric local binary patterns (CS-LBPs), can address such issues. 
+        In this paper, a new model based on CS-LBPs is proposed called center-symmetric local binary convolutional neural networks (CS-LBCNN), which addresses the issues of LBCNNs. 
+        Furthermore, an enhanced version of CS-LBCNNs called threshold center-symmetric local binary convolutional neural networks (TCS-LBCNNs) is proposed, which addresses another issue related to the zero-thresholding function. 
+        Finally, the proposed models are compared to state-of-the-art models, proving their ability by producing a more accurate and significant classification rate than the existing LBCNN models. 
+        For the bilingual dataset, the TCS-LBCNN enhances the accuracy of the LBCNN and CS-LBCNN by 0.15% and 0.03%, respectively. 
+        In addition, the comparison shows that the accuracy acquired by the TCS-LBCNN is the second-highest using the MNIST and MADBase datasets.
+    </p>
+
+    <hr>
+
+    <h3>Paper Download</h3>
+    <p><a href="https://doi.org/10.1016/j.knosys.2022.110079">https://doi.org/10.1016/j.knosys.2022.110079</a></p>
+
+    <h2>Research Aims and Objectives</h2>
+    <p>This research aims to enhance the performance of LBP-based convolutional neural networks on the automatic recognition of bilingual handwriting. The objectives of the research are as follows:</p>
+    <ol>
+        <li>To introduce center-symmetric local binary convolutional neural networks (CS-LBCNNs) to overcome the illumination transformation and the negative effect of the random weights of LBCNNs.</li>
+        <li>To enhance the CS-LBCNN by applying a nonzero thresholding function that allows the model to extract more distinguished features, called the threshold center-symmetric local binary convolutional neural network (TCS-LBCNN) model.</li>
+        <li>To validate the models with other benchmark models.</li>
+    </ol>
+
+    <hr>
+
+    <h2>References</h2>
+    <p>Al-wajih, Ebrahim, and Rozaida Ghazali. "Threshold center-symmetric local binary convolutional neural networks for bilingual handwritten digit recognition." Knowledge-Based Systems (2022): 110079.</p>
+    <pre>
+<code>@article{al2022threshold,
+  title={Threshold center-symmetric local binary convolutional neural networks for bilingual handwritten digit recognition},
+  author={Al-wajih, Ebrahim and Ghazali, Rozaida},
+  journal={Knowledge-Based Systems},
+  pages={110079},
+  year={2022},
+  publisher={Elsevier}
+}
+</code>
+    </pre>
+
+    <hr>
+
+    <h3>Requirements</h3>
+    <p>See the <a href="INSTALL.md">installation instructions</a> for a step-by-step guide.</p>
+</body>
+</html>