Torch implementation of - Threshold Center-Symmetric Local Binary Convolutional Neural Networks for Bilingual Handwritten Digit Recognition
+ + ++ 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. +
+ +https://doi.org/10.1016/j.knosys.2022.110079
+ +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:
+Al-wajih, Ebrahim, and Rozaida Ghazali. "Threshold center-symmetric local binary convolutional neural networks for bilingual handwritten digit recognition." Knowledge-Based Systems (2022): 110079.
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+@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}
+}
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+ See the installation instructions for a step-by-step guide.
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