Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Recent Advances on Soft Computing and Data Mining (SCDM 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 978))

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. AlKhateeb JH, Alseid M (2014) DBN-based learning for Arabic handwritten digit recognition using DCT features. In: 2014 6th international conference on computer science and information technology (CSIT). IEEE, pp 222–226

    Google Scholar 

  2. Chatterjee A, Malakar S, Sarkar R, Nasipuri M (2018) Handwritten digit recognition using DAISY descriptor: a study. In: 2018 Fifth international conference on emerging applications of information technology (EAIT). IEEE, pp 1–4

    Google Scholar 

  3. Lawgali A (2015) Handwritten Digit Recognition based on DWT and DCT. Int J Database Theory Appl 8:215–222

    Article  Google Scholar 

  4. El-Sherif EA, Abdelazeem S (2007) A two-stage system for Arabic handwritten digit recognition tested on a new large database. In: Artificial intelligence and pattern recognition, pp 237–242

    Google Scholar 

  5. Mozafari M, Ganjtabesh M, Nowzari-Dalini A, Thorpe SJ, Masquelier T (2018) Bio-inspired digit recognition using spike-timing-dependent plasticity (stdp) and reward-modulated stdp in deep convolutional networks. ArXiv Preprint. https://arxiv.org/abs/180400227

  6. Chakraborty S, Paul S, Sarkar R, Nasipuri M (2019) Feature map reduction in CNN for handwritten digit recognition. In: Recent developments in machine learning and data analytics. Springer, Singapore, pp 143–148

    Google Scholar 

  7. Siddique F, Sakib S, Siddique MAB (2019) Handwritten digit recognition using convolutional neural network in Python with tensorflow and observe the variation of accuracies for various hidden layers

    Google Scholar 

  8. Juefei-Xu F, Boddeti VN, Savvides M (2016) Local binary convolutional neural networks. ArXiv Preprint. https://arxiv.org/abs/160806049

  9. Das A, Kundu T, Saravanan C (2018) Dimensionality reduction for handwritten digit recognition

    Google Scholar 

  10. Kanoun S, Ennaji A, LeCourtier Y, Alimi AM (2002) Script and nature differentiation for Arabic and Latin text images. In: 2002 Proceedings of eighth international workshop on frontiers in handwriting recognition. IEEE, pp 309–313

    Google Scholar 

  11. Haboubi S, Maddouri SS, Amiri H (2011) Discrimination between Arabic and Latin from bilingual documents. In: 2011 International conference on communications, computing and control applications (CCCA). IEEE, pp 1–6

    Google Scholar 

  12. Al-wajih E, Ahmed M Extended statistical features based on gabor filters for face-based gender classification: classifiers performance comparison. 10

    Google Scholar 

  13. Al-wajih E, Ghouti L (2019) Gender recognition using four statistical feature techniques: a comparative study of performance. Evol Intell. https://doi.org/10.1007/s12065-019-00264-z

    Article  Google Scholar 

  14. Waheeb W, Ghazali R, Deris MM (2015) Content-based sms spam filtering based on the scaled conjugate gradient backpropagation algorithm. In: 2015 12th International conference on fuzzy systems and knowledge discovery (FSKD). IEEE, pp 675–680

    Google Scholar 

  15. Lawrence S, Giles CL, Tsoi AC, Back AD (1997) Face recognition: a convolutional neural-network approach. IEEE Trans Neural Netw 8:98–113

    Article  Google Scholar 

  16. LeCun Y (1989) Generalization and network design strategies. Connect Perspect 143–155

    Google Scholar 

  17. El-Sawy A, Hazem EB, Loey M (2016) CNN for handwritten arabic digits recognition based on LeNet-5. In: International conference on advanced intelligent systems and informatics. Springer, Cham, pp 566–575

    Google Scholar 

  18. LeCun Y (1998) The MNIST database of handwritten digits. Httpyann Lecun Comexdbmnist

    Google Scholar 

  19. Dhandra BV, Hangarge M, Mukarambi G (2010) Spatial features for handwritten Kannada and English character recognition. IJCA Spec Issue RTIPPR 3:146–151

    Google Scholar 

  20. Dhandra BV, Mukarambi G, Hangarge M (2011) A recognition system for handwritten Kannada and English characters. Int J Comput Vis Robot 2:290–301

    Article  Google Scholar 

  21. Dhandra BV, Mukarambi G, Hangarge M (2011) A script independent approach for handwritten bilingual Kannada and Telugu digits recognition. Int J Mach Intell 3

    Google Scholar 

  22. Chaudhari SA, Gulati RM (2013) An OCR for separation and identification of mixed English—Gujarati digits using kNN classifier. In: 2013 International conference on intelligent systems and signal processing (ISSP). IEEE, pp 190–193

    Google Scholar 

  23. Lehal G, Bhatt N (2000) A recognition system for Devnagri and English handwritten numerals. In: International Conference on Multimodal Interfaces—ICMI 2000, pp 442–449

    Google Scholar 

  24. Elgammal AM, Ismail MA (2001) Techniques for language identification for hybrid Arabic-English document images. In: 2001 Proceedings of sixth international conference on document analysis and recognition. IEEE, pp 1100–1104

    Google Scholar 

  25. Guo H, Ding X, Zhang Z, Guo F, Wu Y (1995) Realization of a high-performance bilingual Chinese-English OCR system. In: 1995 Proceedings of the third international conference on document analysis and recognition. IEEE, pp 978–981

    Google Scholar 

  26. Jawahar CV, Kumar MP, Kiran SR (2003) A bilingual OCR for Hindi-Telugu documents and its applications. In: 2003 Proceedings of seventh international conference on document analysis and recognition. IEEE, pp 408–412

    Google Scholar 

  27. Wang K, Jin J, Wang Q (2009) High performance Chinese/English mixed OCR with character level language identification. In: 2009 10th International conference on document analysis and recognition, ICDAR’09. IEEE, pp 406–410

    Google Scholar 

  28. Hangarge M, Dhandra BV (2010) Offline handwritten script identification in document images. Int J Comput Appl 4:6–10

    Google Scholar 

  29. Pal U, Chaudhuri BB (2001) Automatic identification of English, Chinese, Arabic, Devnagari and Bangla script line. In: 2001 Proceedings of sixth international conference on document analysis and recognition. IEEE, pp 790–794

    Google Scholar 

  30. Dhandra BV, Benne RG, Hangarge M (2011) Kannada, Telugu and Devanagari handwritten numeral recognition with probabilistic neural network: a script independent approach. Int J Comput Appl 26:11–16

    Google Scholar 

  31. Singhal V, Navin N, Ghosh D (2003) Script-based classification of hand-written text documents in a multilingual environment. In: 2003 Proceedings of 13th international workshop on research issues in data engineering: multi-lingual information management, RIDE-MLIM 2003. IEEE, pp 47–54

    Google Scholar 

  32. Rajput GG, Anita HB (2010) Handwritten script recognition using DCT and wavelet features at block level. IJCA Spec Issue RTIPPR 3:158–163

    Google Scholar 

  33. He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: European conference on computer vision. Springer, Cham, pp 630–645

    Chapter  Google Scholar 

  34. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324

    Article  Google Scholar 

  35. Ojala T, Pietikainen M, Harwood D (1994) Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: 1994 Proceedings of the 12th IAPR international conference on pattern recognition, conference a: computer vision & image processing, vol 1. IEEE, pp 582–585

    Google Scholar 

Download references

Acknowledgment

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.

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, 86400, Parit Raja, Johor, Malaysia

    Ebrahim Al-wajih, Rozaida Ghazali & Yana Mazwin Mohmad Hassim

  2. Society Development & Continuing Education Center, Hodeidah University, 3114, Alduraihimi, Hodeidah, Yemen

    Ebrahim Al-wajih

Authors
  1. Ebrahim Al-wajih
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rozaida Ghazali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yana Mazwin Mohmad Hassim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ebrahim Al-wajih .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Rozaida Ghazali

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Nazri Mohd Nawi

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Mustafa Mat Deris

  4. School of Information Technology, Deakin University, Geelong Waurn Ponds Campus, VIC, Australia

    Jemal H. Abawajy

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Al-wajih, E., Ghazali, R., Hassim, Y.M.M. (2020). Residual Neural Network Vs Local Binary Convolutional Neural Networks for Bilingual Handwritten Digit Recognition. In: Ghazali, R., Nawi, N., Deris, M., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2020. Advances in Intelligent Systems and Computing, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-030-36056-6_3

Download citation

Publish with us

Policies and ethics

Search

Navigation