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A pragmatic convolutional bagging ensemble learning for recognition of Farsi handwritten digits

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Abstract

Recognition of handwritten digits is one of the most important and challenging issues in recent decades in the field of computer science. Its cursive nature, the right to left writing styles of words and characters as well as various digits shapes have imposed curiosity among numerous researchers to impose a lot of efforts on the recognition of handwritten Farsi numbers. In order to improve the recognition accuracy of Farsi handwritten digit recognition, the pragmatic CBWME network structure model based on convolution bagging weighted majority ensemble learning is developed by integrating the convolution neural network (CNN) and bagging weighted majority ensemble learning. For base classifiers, we applied the VGG16, ResNet18, and Xception architectures and explored the bagging weighted majority ensemble learning in combining the base classifiers results, which are later used in identifying Farsi handwritten digits. The performance of the CNN models (VGG16, ResNet18, and Xception) and CBWME model was evaluated by comparing their results. From the experimental result analysis, it was observed that the proposed CBWME model achieved the best average recognition accuracy (97.65%), followed by the Xception model (95.9%), ResNet18 model (93.75%), and VGG16 model (90.26%) in HODA dataset. The accuracy orders were the same as in IFHCDB and CENPARMI datasets. The CSE model attained the best result with rate of (99.876%) compared with the other studies.

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References

  1. Sajedi H, Bahador M (2016) Persian handwritten number recognition using adapted framing feature and support vector machines. Int J Comput Intell Appl 15(1):165–180

    Article  Google Scholar 

  2. Karimi H, Esfahanimehr A, Mosleh M, Salehpour S, Medhati O (2015) Persian handwritten digit recognition using ensemble classifiers. Procedia Comput Sci 73(1):416–425

    Article  Google Scholar 

  3. Khosravi H, Kabir E (2006) Introduction of Two Fast and Effective Features for Handwritten Farsi Digit Recognition. In: 2006 4th Iranian Conference on Machine Vision and Image Processing (MVIP), pp 87–95

  4. Jain V, Dubey A, Gupta A, Sharma S (2016) Comparative Analysis of Machine Learning Algorithms in OCR. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), IEEE, pp 1089–1092

  5. Chaudhary D, Sharma K (2019) Hindi Handwritten Character Recognition Using Deep Convolution Neural Network. In: 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom), IEEE, pp 961–965

  6. Al-Janabi S, Alkaim A (2020) A nifty collaborative analysis to predicting a novel tool (DRFLLS) for missing values estimation. Soft Comput 24(1):555–569

    Article  Google Scholar 

  7. Kilvisharam OMA, Poruran S, Caffiyar MY (2020) A novel deep convolutional neural network architecture based on transfer learning for handwritten Urdu character recognition. VJESN 27(4):1160–1165

    Google Scholar 

  8. Granet A, Morin E, Mouchère H, Quiniou S, Viard-Gaudin C (2018) Transfer Learning for Handwriting Recognition on Historical Documents. In: 2018 International Conference on Pattern Recognition Application & Methods (ICPRAM), pp 432–439

  9. Jaderberg M, Simonyan K, Vedaldi A, Zisserman A (2014) Deep structured output learning for unconstrained text recognition. arXiv preprint http://arXiv:1412.5903

  10. Farahbakhsh E, Kozegar E, Soryani M (2017) Improving Persian Digit Recognition by Combining Data Augmentation and AlexNet. In: 2017 10th Iranian Conference on Machine Vision and Image Processing (ICMVIP), pp 265–270

  11. 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). IEEE, pp 90–95

  12. Nanehkaran YA, Zhang D, Salimi S, Chen J, Tian Y, Al-Nabhan N (2020) Analysis and comparison of machine learning classifiers and deep neural networks techniques for recognition of farsi handwritten digits. J Supercomput 1(3):1–30

    Google Scholar 

  13. Akhlaghi M, Ghods V (2020) Farsi handwritten phone number recognition using deep learning. SN Appl Sci 2(3):1–10

    Article  Google Scholar 

  14. Elkhayati M, Elkettani Y (2020) Towards directing convolutional neural networks using computational geometry algorithms: application to handwritten Arabic character recognition. Adv Sci Technol Eng 5(5):137–147

    Google Scholar 

  15. Sahlol AT, Elaziz M, Qaness MA, Kim S (2020) Handwritten Arabic optical character recognition approach based on hybrid whale optimization algorithm with neighborhood rough set. IEEE Access 8(2):23011–23021

    Article  Google Scholar 

  16. Safarzadeh VM, Jafarzadeh P (2020) Offline Persian Handwriting Recognition with CNN and RNN-CTC. In: 25th International Computer Conference Computer Society of Iran (CSI), pp 1–10

  17. Modhej N, Bastanfard A, Teshnehlab M, Raiesdana S (2020) Pattern separation network based on the hippocampus activity for handwritten recognition. IEEE Access 8:212803–212817

    Article  Google Scholar 

  18. Parseh M, Rahmanimanesh M, Keshavarzi P (2020) Persian handwritten digit recognition using combination of convolutional neural network and support vector machine methods. Int Arab J Inf Technol 17(4):572–578

    Google Scholar 

  19. Janabi S, Mahdi MA (2019) Evaluation prediction techniques to achievement an optimal biomedical analysis. Int J Grid Util Comput 10(5):512–527

    Article  Google Scholar 

  20. Moradi V, Razzazi F, Behrad A (2016) Recognition of handwritten persian two-digit numerals using a novel hybrid SVM/HMM algorithm. Majlesi J Electr Eng 10(3):233–245

    Google Scholar 

  21. Parvin H, Alizadeh H, Bidgoli B (2010) A new divide and conquer based classification for OCR. Converg Hybrid Inform Technol 3(1):1–1

    Google Scholar 

  22. Janabi S, Alkaim AF, Adel Z (2020) An innovative synthesis of deep learning techniques (DCapsNet & DCOM) for generation electrical renewable energy from wind energy. Soft Comput 24(14):10943–10962

    Article  Google Scholar 

  23. Oh B, Lee J (2018) A Case Study on Scene Recognition Using an Ensemble Convolution Neural Network. In: 2018 20th International Conference on Advanced Communication Technology (ICACT), pp 351–353

  24. Agahi H, Mahmoodzadeh A, Salehi M (2018) Handwritten digits recognition using an ensemble technique based on the firefly algorithm. J Inf Syst Telecommun 3(1):123–136

    Google Scholar 

  25. Ameryan M, Schomaker L (2019) A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification. arXiv preprint arXiv: 1912.03223

  26. Janabi S, Mohammad M, Sultan A (2020) A new method for prediction of air pollution based on intelligent computation. Soft Comput 24(1):661–680

    Article  Google Scholar 

  27. Janabi S, Shourbaji I, Shojafar M, Abdelhag M (2017) Mobile Cloud Computing: Challenges and Future Research Directions. In: 2017 10th International Conference on Developments in E-Systems Engineering, IEEE, pp 62–67

  28. Mahdi MA, Janabi S (2019) A Novel Software to Improve Healthcare Base on Predictive Analytics and Mobile Services for Cloud Data Centers. In: International Conference on Big Data and Networks Technologies, pp 320–339

  29. Goswami S, Passi K (2019) Real time Static Gesture Detection Using Deep Learning. In: 2019 7th International Conference on Big Data Analytics, pp 408–426

  30. Hoffman J (2019) Cramnet: layer-wise deep neural network compression with knowledge transfer from a teacher network. arXiv preprint arXiv: 1904.05982

  31. He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition. In:2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp 770–778

  32. Liu Y, Yang J (2019) Face Recognition Method Based on Convolutional Neural Network. In: International Conference in Communications, Signal Processing, and Systems (CSS), pp 1925–1929

  33. Chollet F (2016) Xception: Deep Learning with Depth-wise Separable Convolutions. In:2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1251–1258

  34. Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning Deep Features for Discriminative Localization. In:2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2921–2929

  35. Jinsakul N, Tsai CF, Tsai CE, Wu P (2019) Enhancement of deep learning in image classification performance using xception with the swish activation function for colorectal polyp preliminary screening. Mathematics 7(12):1170–1182

    Article  Google Scholar 

  36. 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. arXiv preprint arXiv: 1704.04861

  37. Dehghanian A, Ghods V (2018) Farsi Handwriting Digit Recognition Based on Convolutional Neural Networks. In: 2018 6th International Symposium on Computational and Business Intelligence (CBI), IEEE, pp 65–68

  38. Alizadehashraf B, Roohi S (2017) Persian Handwritten Character Recognition Using Convolutional Neural Network. In: 2017 10th Iranian Conference on Machine Vision and Image Processing (MVIP), pp 247–251

  39. Rani NS, Subramani AC, Kumar A, Pushpa BR (2020) Deep Learning Network Architecture Based Kannada Handwritten Character Recognition. In: 2020 Second International Conference on Inventive Research in Computing Applications (IRCA), IEEE, pp 213–220

  40. Le CY, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

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Acknowledgements

The writers want to appreciate Pzcnet Ltd. (http://www.pzcnet.com/). The author also likes to appreciate all the judges and editors whose useful suggestions helped improve the article.

Funding

This work is partly supported by grants from the National Natural Science Foundation of China (Project no. 61672439) and the Fundamental Research Funds for the Central Universities (#20720181004).

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Authors and Affiliations

  1. School of Informatics, Xiamen University, Xiamen, 361005, Fujian, People’s Republic of China

    Y. A. Nanehkaran, Junde Chen & Defu Zhang

  2. School of Information Engineering, Jiangxi University of Science and Technology, 86, Hongqi Ave, Ganzhou, Jiangxi, People’s Republic of China

    Y. A. Nanehkaran

  3. Department of Electrical and Computer Engineering, Seraj Institute of Higher Education, Tabriz, Iran

    Soheil Salimi

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  1. Y. A. Nanehkaran
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  2. Junde Chen
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  3. Soheil Salimi
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Correspondence to Defu Zhang.

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Nanehkaran, Y.A., Chen, J., Salimi, S. et al. A pragmatic convolutional bagging ensemble learning for recognition of Farsi handwritten digits. J Supercomput 77, 13474–13493 (2021). https://doi.org/10.1007/s11227-021-03822-4

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  • DOI: https://doi.org/10.1007/s11227-021-03822-4

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