Skip to main content
SpringerLink
Log in

Ensemble deep transfer learning model for Arabic (Indian) handwritten digit recognition

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Recognising handwritten digits or characters is a challenging task due to noisy data that results from different writing styles. Numerous applications essentially motivate to build an effective recognising model for such purposes by utilizing recent intelligent techniques. However, the difficulty emerges when using the Arabic language that suffers from diverse noises; because of the way of writing inherent in connecting characters and digits. Therefore, this work focuses on the Arabic (Indian) digits and propose an ensemble deep transfer learning (EDTL) model that efficaciously detect and recognise these digits. The EDTL model is a combination of two effective pre-trained transfer learning models that consume time and cost complexity in the training phase. The EDTL is trained on large datasets to extract relevant features as input to a fully-connected Artificial Neural Network classifier. The experimental results, using popular datasets, show significant performance obtained by the EDTL model with accuracy reached up to 99.83% in comparison to baseline methods include deep transfer learning models, ensemble deep transfer learning models and state-of-the-art techniques.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. http://datacenter.aucegypt.edu/shazeem/.

  2. http://yann.lecun.com/exdb/mnist/.

  3. https://keras.io/.

References

  1. Abdleazeem S, El-Sherif E (2008) Arabic handwritten digit recognition. Int J Doc Anal Recognit (IJDAR) 11(3):127–141

    Article  Google Scholar 

  2. Abuowaida S, Chan H, Alshdaifat N, Abualigah L (2021) A novel instance segmentation algorithm based on improved deep learning algorithm for multi-object images. Jordan J Comput Inf Technol 07:1. https://doi.org/10.5455/jjcit.71-1603701313

    Article  Google Scholar 

  3. Al-wajih E, Ghazali R (2021) An enhanced lbp-based technique with various size of sliding window approach for handwritten arabic digit recognition. Multimed Tools Appl. https://doi.org/10.1007/s11042-021-10762-x

    Article  Google Scholar 

  4. Alani AA (2017) Arabic handwritten digit recognition based on restricted boltzmann machine and convolutional neural networks. Information. https://doi.org/10.3390/info8040142

    Article  Google Scholar 

  5. Alkhateeb JH (2020) Handwritten arabic digit recognition using convolutional neural network. Int J Commun Netw Inf Secur 12(3):411–416

    Google Scholar 

  6. Alkhawaldeh RS (2019) Dgr: gender recognition of human speech using one-dimensional conventional neural network. Sci Program. https://doi.org/10.1155/2019/7213717

    Article  Google Scholar 

  7. Alkhawaldeh RS (2020) Arabic (indian) digit handwritten recognition using recurrent transfer deep architecture. Soft Computing pp. 1–11

  8. Alkhawaldeh RS, Khawaldeh S, Pervaiz U, Alawida M, Alkhawaldeh H (2019) Niml: non-intrusive machine learning-based speech quality prediction on voip networks. IET Commun 13(16):2609–2616

    Article  Google Scholar 

  9. Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Hasan M, Van Essen BC, Awwal AA, Asari VK (2019) A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3):292

    Article  Google Scholar 

  10. Alshdaifat NFF, Talib AZ, Osman MA (2020) Improved deep learning framework for fish segmentation in underwater videos. Ecol Inf 59:101121

    Article  Google Scholar 

  11. Altaf F, Islam SM, Janjua NK (2021) A novel augmented deep transfer learning for classification of covid-19 and other thoracic diseases from x-rays. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06044-0

    Article  Google Scholar 

  12. Ashiquzzaman A, Tushar AK (2017) Handwritten arabic numeral recognition using deep learning neural networks. In: 2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR), IEEE, pp. 1–4

  13. Ashiquzzaman A, Tushar AK, Rahman A, Mohsin F (2019) An efficient recognition method for handwritten arabic numerals using cnn with data augmentation and dropout. In: Balas VE, Sharma N, Chakrabarti A (eds) Data management, analytics and innovation. Springer Singapore, Singapore, pp 299–309

    Chapter  Google Scholar 

  14. Ashiquzzaman A, Tushar AK, Rahman A, Mohsin F (2019) An efficient recognition method for handwritten arabic numerals using cnn with data augmentation and dropout. Data management, analytics and innovation. Springer, Berlin, pp 299–309

    Chapter  Google Scholar 

  15. Boufenar C, Kerboua A, Batouche M (2018) Investigation on deep learning for off-line handwritten arabic character recognition. Cognit Syst Res 50:180–195

    Article  Google Scholar 

  16. Can YS, Kabaday ME (2020) Automatic cnn-based arabic numeral spotting and handwritten digit recognition by using deep transfer learning in ottoman population registers. Appl Sci. https://doi.org/10.3390/app10165430

    Article  Google Scholar 

  17. Chollet F (2018) Deep learning mit python und keras: das praxis-handbuch vom entwickler der keras-bibliothek. MITP-Verlags GmbH & Co, KG

    Google Scholar 

  18. Dietterich TG (2000) Ensemble methods in machine learning. In: International workshop on multiple classifier systems, pp. 1–15. Springer

  19. Dua M, Singla R, Raj S, Jangra A et al (2021) Deep cnn models-based ensemble approach to driver drowsiness detection. Neural Comput Appl 33(8):3155–3168

    Article  Google Scholar 

  20. El-Sawy A, EL-Bakry H, Loey M (2017) Cnn for handwritten arabic digits recognition based on lenet-5. In: A.E. Hassanien, K. Shaalan, T. Gaber, A.T. Azar, M.F. Tolba (eds.) Proceedings of the international conference on advanced intelligent systems and informatics 2016. Springer International Publishing, Cham. pp. 566–575

  21. Giovannetti A, Susi G, Casti P, Mencattini A, Pusil S, López ME, Di Natale C, Martinelli E (2021) Deep-meg: spatiotemporal cnn features and multiband ensemble classification for predicting the early signs of alzheimers disease with magnetoencephalography. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06105-4

    Article  Google Scholar 

  22. Gupta D, Bag S (2021) Cnn-based multilingual handwritten numeral recognition: a fusion-free approach. Expert Syst Appl 165:113784. https://doi.org/10.1016/j.eswa.2020.113784

    Article  Google Scholar 

  23. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778

  24. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications

  25. Khawaldeh S, Pervaiz U, Rafiq A, Alkhawaldeh RS (2018) Noninvasive grading of glioma tumor using magnetic resonance imaging with convolutional neural networks. Appl Sci 8(1):27

    Article  Google Scholar 

  26. Latif G, Alghazo J, Alzubaidi L, Naseer MM, Alghazo Y (2018) Deep convolutional neural network for recognition of unified multi-language handwritten numerals. In: 2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR), pp. 90–95 . https://doi.org/10.1109/ASAR.2018.8480289

  27. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436

    Article  Google Scholar 

  28. Loey M, El-Sawy A, El-Bakry H (2017) Deep learning autoencoder approach for handwritten arabic digits recognition. arXiv preprint arXiv:1706.06720

  29. Lumini A, Nanni L (2019) Deep learning and transfer learning features for plankton classification. Ecol Inform 51:33–43

    Article  Google Scholar 

  30. Maheshwari M, Namdev D, Maheshwari S (2018) A systematic review of automation in handwritten character recognition. Int J Appl Eng Res 13(10):8090–8099

    Google Scholar 

  31. Mahmoud S (2008) Recognition of writer-independent off-line handwritten arabic (indian) numerals using hidden markov models. Signal Process 88(4):844–857

    Article  Google Scholar 

  32. Minetto R, Segundo MP, Sarkar S (2019) Hydra: an ensemble of convolutional neural networks for geospatial land classification. IEEE Trans Geosci Remote Sens 57(9):6530–6541

    Article  Google Scholar 

  33. Mudhsh M, Almodfer R (2017) Arabic handwritten alphanumeric character recognition using very deep neural network. Information 8(3):105

    Article  Google Scholar 

  34. Owusu E, Wiafe I (2021) An advance ensemble classification for object recognition. Neural Comput Appl. https://doi.org/10.1007/s00521-021-05881-3

    Article  Google Scholar 

  35. Perez L, Wang J (2017) The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621

  36. Rawat W, Wang Z (2017) Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput 29(9):2352–2449

    Article  MathSciNet  Google Scholar 

  37. Rehman A, Naz S, Razzak MI, Hameed IA (2019) Automatic visual features for writer identification: a deep learning approach. IEEE Access 7:17149–17157

    Article  Google Scholar 

  38. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4510–4520

  39. Shahabi MS, Maghsoudi A (2021) Prediction of drug response in major depressive disorder using ensemble of transfer learning with convolutional neural network based on eeg. Biocybernet Biomed Eng 41:946–959

    Article  Google Scholar 

  40. de Sousa IP (2018) Convolutional ensembles for arabic handwritten character and digit recognition. PeerJ Comput Sci 4:e167

    Article  Google Scholar 

  41. Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E (2018) Deep learning for computer vision: a brief review. Comput Intell Neurosci. https://doi.org/10.1155/2018/7068349

    Article  Google Scholar 

Download references

Funding

This work is not funded.

Author information

Authors and Affiliations

  1. Department of Computer Information Systems, The University of Jordan, Aqaba, 77110, Jordan

    Rami S. Alkhawaldeh

  2. School of Computer Sciences, Universiti Sains Malaysia (USM), 11800, Gelugor, Penang, Malaysia

    Moatsum Alawida

  3. Department of Computer Sciences, Abu Dhabi University, Abu Dhabi, UAE

    Moatsum Alawida

  4. Department of Computer Science, Prince Hussein Bin Abdullah, Faculty of Information Technology, Al al-Bayt University, Mafraq, 25113, Jordan

    Nawaf Farhan Funkur Alshdaifat

  5. Department of Basic Sciences, Faculty of Science, The Hashemite University, Zarqa, Jordan

    Wafa’ Za’al Alma’aitah

  6. Department of Management Information Systems, Al-Balqa Applied University, Salt, 19117, Jordan

    Ammar Almasri

Authors
  1. Rami S. Alkhawaldeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Moatsum Alawida
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nawaf Farhan Funkur Alshdaifat
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wafa’ Za’al Alma’aitah
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ammar Almasri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rami S. Alkhawaldeh.

Ethics declarations

Conflict of interest

The author(s) declare(s) that there is no conflict of interest regarding the publication of this paper.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alkhawaldeh, R.S., Alawida, M., Alshdaifat, N.F.F. et al. Ensemble deep transfer learning model for Arabic (Indian) handwritten digit recognition. Neural Comput & Applic 34, 705–719 (2022). https://doi.org/10.1007/s00521-021-06423-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06423-7

Keywords

Search

Navigation