Residual Neural Network Vs Local Binary Convolutional Neural Networks for Bilingual Handwritten Digit Recognition

  • Ebrahim Al-wajihEmail author
  • Rozaida Ghazali
  • Yana Mazwin Mohmad Hassim
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 978)


Most of the public and government documents in Arabic states are typed or written in bilingual forms; Arabic and Latin languages; such as railway reservation slips, bank withdrawal slips, etc. Using a bilingual system is better than using two systems for every language that is not a practical solution. In this paper, a bilingual digit recognition system is developed using Residual Neural Network (ResNet) and Local Binary Convolutional Neural Networks (LBCNN). The proposed systems are evaluated using a bilingual dataset generated from AHDBase and MNIST datasets. The recognition rate of ResNet or LBCNN is 99.38%. In addition, the proposed systems are applied to MNIST and AHDBase datasets separately. The obtained accuracies for MNIST are 99.27% and 99.51% and for AHDBase are 99.29% and 99.38%, respectively. The resulting performance of ResNet and LBCNN are the highest when they are compared against several state-of-the-art techniques.


Bilingual digit recognition Deep learning Residual Neural Network Local binary convolutional neural network 



The authors would like to thank Universiti Tun Hussein Onn Malaysia (UTHM) and Ministry of Education Malaysia for financially supporting this research under the Fundamental Research Grant Scheme (FRGS), Vote No. 1641.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ebrahim Al-wajih
    • 1
    • 2
    Email author
  • Rozaida Ghazali
    • 1
  • Yana Mazwin Mohmad Hassim
    • 1
  1. 1.Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaParit RajaMalaysia
  2. 2.Society Development & Continuing Education CenterHodeidah UniversityAlduraihimiYemen

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