Skip to main content
SpringerLink
Log in

Deep Learning Inspired Nonlinear Classification Methodology for Handwritten Digits Recognition Using DSR Encoder

  • Research Article-Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

The overlapped handwritten digit classification is a global challenge and a significant measure to assess the network recognition ability ratio. Most efficient models have been designed based on convolutional neural networks (CNN) for effective image classification and digit identification. Subsequently, multiple CNN models have inadequate accuracy because of high degree parameter dimensions that lead to abnormal digit detection error rates and computation complexity. We propose a Deep Digit Recognition Network (DDRNet) based on Deep ConvNets to minimize the number of parameters and features to keep the model light while maximizing the accuracy with an adaptive voting (AV) scheme for digit recognition. The individual digit is identified by CNN, and uncertain digits or strings are identified by Deep Convolutional Network (DCN) with AV scheme through Voting-Weight Conditional Random Field (VWCRF) strategy. These methods originated with the YOLO algorithm. The simulations show that our DDRNet approach achieves an accuracy of 99.4% without error fluctuations, in a stable state with less than 15 epochs contrast with state-of-art approaches. Additionally, specific convolution techniques (SqueezeNet, batch normalization) and image augmentation techniques (dropout, back-propagation, and an optimum learning rate) were examined to assess the system performance based on MNIST dataset (available at: http://yann.lecun.com/exdb/mnist/).

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Lu, Z.; Chi, Z.; Siu, W.-C.; Shi, P.: A background-thinning-based approach for separating and recognizing connected handwritten digit strings. Pattern Recogn. 32(6), 921–933 (1999)

    Article  Google Scholar 

  2. Guha, A.; Samanta, D.; Banerjee, A.; Agarwal, D.: A deep learning model for information loss prevention from multi-page digital documents. IEEE Access 1 (2021). https://doi.org/10.1109/ACCESS.2021.3084841

  3. Teow, M.Y.W.: A minimal convolutional neural network for handwritten digit recognition 171–176 (2017)

  4. Gomathy, V.; Padhy, N.; Samanta, D.; Sivaram, M.; Jain, V.; Amiri, I.S.: Malicious node detection using heterogeneous cluster based secure routing protocol (HCBS) in wireless adhoc sensor networks, J. Amb. Intell. Human. Comput. 11(11), 4995–5001 (2020). https://doi.org/10.1007/s12652-020-01797-3. http://link.springer.com/10.1007/s12652-020-01797-3

  5. Hossain, M.A.; Samanta, D.; Sanyal, G.; Extraction of panic expression depending on lip detection 137–141 (2012). https://doi.org/10.1109/ICCS.2012.35

  6. Maheswari, M.; Geetha, S.; Kumar, S.S.; Karuppiah, M.; Samanta, D.; Park, Y.: Pevrm: Probabilistic evolution based version recommendation model for mobile applications. IEEE Access 9, 20819–20827 (2021). https://doi.org/10.1109/ACCESS.2021.3053583

    Article  Google Scholar 

  7. Mekala, M.; Park, W.; Dhiman, G.; Srivastava, G.; Park, J.H.; Jung, H.-Y.: Deep learning inspired object consolidation approaches using lidar data for autonomous driving: a review. Arch. Comput. Methods Eng. 1–21 (2021)

  8. MS, R.; Patan, A.H.; Gandomi, J. H.; Park, H.-Y.; Jung A drl based 4-r computation model for object detection on rsu using lidar in ilot. In: IEEE Symposium Series on Computational Intelligence (SSCI), pp. 01–08 . IEEE (2021)

  9. Hochuli, A.G.; Britto, A.S., Jr.; Saji, D.A.; Saavedra, J.M.; Sabourin, R.; Oliveira, L.S.: A comprehensive comparison of end-to-end approaches for handwritten digit string recognition. Expert Syst. Appl. 165, 114196 (2021)

    Article  Google Scholar 

  10. Shaukat, Z.; Ali, S.; Xiao, C.; Sahiba, S.; Ditta, A.; et al.: Cloud-based efficient scheme for handwritten digit recognition. Multim. Tools Appl. 79(39), 29537–29549 (2020)

    Article  Google Scholar 

  11. Zhenwei, S.: Datefinder: detecting date regions on handwritten document images based on positional expectancy, Ph.D. thesis, Faculty of Science and Engineering (2016)

  12. Saabni, R.: Recognizing handwritten single digits and digit strings using deep architecture of neural networks. In: Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR), pp. 1–6. IEEE (2016)

  13. Kusetogullari, H.; Yavariabdi, A.; Hall, J.; Lavesson, N.: Digitnet: a deep handwritten digit detection and recognition method using a new historical handwritten digit dataset. Big Data Res. 23, 100182 (2021)

  14. Hochuli, A.G.; Oliveira, L.S.; Britto, A., Jr.; Sabourin, R.: Handwritten digit segmentation: is it still necessary? Pattern Recogn. 78, 1–11 (2018)

  15. Granell, E.; Chammas, E.; Likforman-Sulem, L.; Martínez-Hinarejos, C.-D.; Mokbel, C.; Cîrstea, B.-I.: Transcription of spanish historical handwritten documents with deep neural networks. J. Imaging 4(1), 15 (2018)

  16. Aly, S.; Almotairi, S.: Deep convolutional self-organizing map network for robust handwritten digit recognition. IEEE Access 8, 107035–107045 (2020)

    Article  Google Scholar 

  17. Jebril, N.A.; Al-Zoubi, H.R.; Al-Haija, Q.A.: Recognition of handwritten arabic characters using histograms of oriented gradient (hog). Pattern Recogn. Image Anal. 28(2), 321–345 (2018)

  18. Ahlawat, S.; Choudhary, A.: Hybrid cnn-svm classifier for handwritten digit recognition. Proc. Comput. Sci. 167, 2554–2560 (2020)

  19. Krishnan, P.; Jawahar, C.: Hwnet v2: an efficient word image representation for handwritten documents. Int. J. Doc. Anal. Recogn. 22(4), 387–405 (2019)

  20. Dhiman, MS, G.; Patan, R.; Kallam, S.; Ramana, K.; Yadav, K.; Alharbi, A.O.: Deep learning-influenced joint vehicle-to-infrastructure and vehicle-to-vehicle communication approach for internet of vehicles. Exp. Syst. e12815 (2021)

  21. Khaja, M.; Kumar, S.; Jain, A.K.; Ahmed, S.; Analysis and simulation of handwritten recognition system. Mater. Today Proc. (2021)

  22. Sharma, R.; Kaushik, B.: Offline recognition of handwritten indic scripts: A state-of-the-art survey and future perspectives. Comput. Sci. Rev. 38, 100302 (2020)

  23. Abdulrazzaq, M.B.; Saeed, J.N.: A comparison of three classification algorithms for handwritten digit recognition, in: 2019 International Conference on Advanced Science and Engineering (ICOASE), pp. 58–63. IEEE (2019)

  24. Qiao, J.; Wang, G.; Li, W.; Chen, M.: An adaptive deep q-learning strategy for handwritten digit recognition. Neural Netw. 107, 61–71 (2018)

    Article  MATH  Google Scholar 

  25. Aras, G.; Makaroğlu, D.; Demir, S.; Cakir, A.: An evaluation of recent neural sequence tagging models in turkish named entity recognition. Expert Syst. Appl. 182, 115049 (2021)

    Article  Google Scholar 

  26. Liu, X.; Zhou, Y.; Wang, Z.: Deep neural network-based recognition of entities in chinese online medical inquiry texts. Futur. Gener. Comput. Syst. 114, 581–604 (2021)

    Article  Google Scholar 

  27. Mekala, M.; Rizwan, P.; Khan, M. S.: Computational intelligent sensor-rank consolidation approach for industrial internet of things (iiot). IEEE Int. Things J. (2021)

  28. Inkeaw, P.; Charoenkwan, P.; Huang, H.-L.; Marukatat, S.; Ho, S.-Y.; Chaijaruwanich, J.: Recognition of handwritten lanna dhamma characters using a set of optimally designed moment features. Int. J. Doc. Anal. Recogn. 20(4), 259–274 (2017)

  29. Iandola, F.N.; Han, S.; Moskewicz, M.W.; Ashraf, K.; Dally, W.J.; Keutzer, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and<0.5mb model size. arXiv:1602.07360

  30. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM

  31. Guha, A.; Samanta, D.: Hybrid approach to document anomaly detection: an application to facilitate RPA in title insurance, Int. J. Autom. Comput. 18(1), 55–72 (2021). https://doi.org/10.1007/s11633-020-1247-y. http://link.springer.com/10.1007/s11633-020-1247-y

  32. De Sousa Neto, A.F.; Bezerra, B.L.D.; Lima, E.B.; Toselli, A.H.: Hdsr-flor: a robust end-to-end system to solve the handwritten digit string recognition problem in real complex scenarios. IEEE Access 8, 208543–208553 (2020). https://doi.org/10.1109/ACCESS.2020.3039003

  33. Siebra Lopes, G.; Clifte da Silva, D.; Oliveira Rodrigues, A.W.; Reboucas Filho, P.P.: Recognition of handwritten digits using the signature features and optimum-path forest classifier. IEEE Latin Am. Trans. 14(5), 2455–2460 (2016). https://doi.org/10.1109/TLA.2016.7530445

  34. Aly, S.; Mohamed, A.: Unknown-length handwritten numeral string recognition using cascade of pca-svmnet classifiers. IEEE Access 7, 52024–52034 (2019). https://doi.org/10.1109/ACCESS.2019.2911851

    Article  Google Scholar 

  35. Gurunath, R., Agarwal, M., Nandi, A., Samanta, D.: An Overview: Security Issue in Iot Network, pp. 104–107 (2018). https://doi.org/10.1109/I-SMAC.2018.8653728

  36. Mekala, D.; Mallah, G.A.; Chaudhry, S.A.: Dawm: Cost-aware asset claim analysis approach on big data analytic computation model for cloud data centre, Sec. Commun. Netw. (2021)

  37. Cecotti, H.; Vajda, S.; Belaïd, A.: High performance classifiers combination for handwritten digit recognition 619–626 (2005)

  38. Mohemmed, A.; Lu, G.; Kasabov, N.: Evaluating span incremental learning for handwritten digit recognition 670–677 (2012)

  39. Jolfaei, MS, A.; Srivastava, G.; Zheng, X.; Anvari-Moghaddam, A.; Viswanathan, P.: Resource offload consolidation based on deep-reinforcement learning approach in cyber-physical systems, IEEE Trans. Emerg. Topics Comput. Intell. (2020)

  40. Kaensar, C.: A comparative study on handwriting digit recognition classifier using neural network, support vector machine and k-nearest neighbor 155–163 (2013)

  41. Le Cun, Y.; Jackel, L.D.; Boser, B.; Denker, J.S.; Graf, H.P.; Guyon, I.; Henderson, D.; Howard, R.E.; Hubbard, W.: Handwritten digit recognition: Applications of neural net chips and automatic learning 303–318 (1990)

  42. Liu, C.-L.; Nakashima, K.; Sako, H.; Fujisawa, H.: Handwritten digit recognition: investigation of normalization and feature extraction techniques. Pattern Recogn. 37(2), 265–279 (2004). https://doi.org/10.1016/S0031-3203(03)00224-3.

    Article  MATH  Google Scholar 

  43. Xi, E.; Bing, S.; Jin, Y.: Capsule network performance on complex data, arXiv preprint arXiv:1712.03480 (2017)

  44. Merabti, H.; Farou, B.; Seridi, H.: A segmentation-recognition approach with a fuzzy-artificial immune system for unconstrained handwritten connected digits. Informatica 42(1), 95–106 (2018)

    MathSciNet  Google Scholar 

  45. Redmon, J.; Farhadi, A.: Yolov3: An incremental improvement, arXiv preprint arXiv:1804.02767 (2018)

Download references

Acknowledgements

This work was supported in part by Basic Science Research Programs of the Ministry of Education (Grant NRF-2018R1A2B 6005105) and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A5A8080290).

Author information

Authors and Affiliations

  1. Department of Computer Science, CHRIST (Deemed to be University), Bangalore, Karnataka, 560029, India

    Divya Singh & Debarata Samanta

  2. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

    Shahana Bano

  3. Department of Computer Science and Engineering, Indian Institute of Information Technology Kalyani, Kalyani, West Bengal, 741235, India

    SK Hafizul Islam

  4. Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38544, Korea

    M. S. Mekala

  5. RLRC for Autonomous Vehicle Parts and Materials Innovation, Yeungnam University, Gyeongsan, 38544, Korea

    M. S. Mekala

Authors
  1. Divya Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shahana Bano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Debarata Samanta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. S. Mekala
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. SK Hafizul Islam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to SK Hafizul Islam.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, D., Bano, S., Samanta, D. et al. Deep Learning Inspired Nonlinear Classification Methodology for Handwritten Digits Recognition Using DSR Encoder. Arab J Sci Eng 48, 1385–1397 (2023). https://doi.org/10.1007/s13369-022-06865-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-022-06865-8

Keywords

Search

Navigation