Skip to main content
SpringerLink
Log in

Offline handwritten Tai Le character recognition using wavelet deep convolution features and ensemble deep variationally sparse Gaussian processes

  • Application of soft computing
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Handwriting recognition is an important application in pattern recognition. Because handwritten Tai Le has similar characters and the proportion of characters with similar shapes is relatively high, this paper proposes a wavelet deep convolution (WDC) feature combined with an ensemble deep variational sparse Gaussian process (EDVSGP) offline handwritten Tai Le recognition method. A Tai Le recognition sample database is constructed, 2 and 3 levels of wavelet decomposition prior features are extracted, and then, the wavelet a priori feature is transformed into a WDC feature. To avoid the dimensional disaster caused by feature fusion, a dimensionality reduction method combining LDA and PCA is used to reducing the dimensionality of the fused features without reducing the accuracy rate. Moreover, to better recognize handwritten Tai Le characters, 6 deep variationally sparse Gaussian process (DVSGP) models are integrated as an EDVSGP classification model. Using the handwritten Tai Le character database, the proposed method is superior to the existing methods, with an accuracy, recall and F1-score of 94.15%, 94.15%, and 94.15%, respectively. The universality of the method is verified on a dataset of handwritten Chinese characters with similar shapes (HCFC2021.4) and the Devanagari dataset.

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

Similar content being viewed by others

Data availability

Enquiries about data availability should be directed to the authors.

References

  • Bhattacharya N, Roy P, Pal U (2018) Sub-stroke-wise relative feature for online indic handwriting recognition. ACM Trans Asian Low-Resour Lang Inf Process 18(2):1–16

    Article  Google Scholar 

  • Chen Z, Yin F, Zhang XY, Liu CL (2020) MuLTReNets: multilingual text recognition networks for simultaneous script identification and handwriting recognition. Pattern Recognit 108:107555

    Article  Google Scholar 

  • Damianou AC, Lawrence ND (2013) Deep Gaussian processes. Computer Science, arXiv:1211.0358

  • Diaz M, Ferrer MA, Quintana JJ (2019) Anthropomorphic features for on-line signatures. IEEE Trans Patt Anal Mach Intell 41(12):2807–2819

    Article  Google Scholar 

  • Gholami A, Kwon K, Wu B, Tai Z, Yue X, Jin P, Zhao S, & Keutzer K (2018) SqueezeNext: hardware-aware neural network design. IEEE Comput Soc Conf Comput Vis Pattern Recogn, Workshops pp 1719–1728. https://doi.org/10.1109/CVPRW.2018.00215

  • Han K, Wang Y, Tian Q, Guo J, Xu C, Xu C (2020) GhostNet: more features from cheap operations. Proc IEEE Comput Soc Conf Comput Vision Patt Recognit 9157333:1577–1586

    Google Scholar 

  • He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. Proc IEEE Comput Soc Conf Comput Vision Pattern Recognit. https://doi.org/10.1109/CVPR.2016.90

    Article  Google Scholar 

  • Hebbal A, Brevault L, Balesdent M et al (2018) Efficient global optimization using deep gaussian processes. IEEE Congr Evol Comput. https://doi.org/10.1109/CEC.2018.8477946

    Article  MATH  Google Scholar 

  • Hensman J, Matthews AGDG, Filippone M et al (2015) MCMC for variationally sparse Gaussian processes. Adv Neural Inf Proces Syst. https://doi.org/10.5555/2969239.2969423

    Article  Google Scholar 

  • Howard A, Sandler GM, Chen LC (2019) Searching for MobileNetV3. IEEE Int Conf Comput Vision 9008835:1314–1324

    Google Scholar 

  • Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. Int Conf Mach Learn 119782:448–456

    Google Scholar 

  • Koriyama T, Kobayashi T (2019) Statistical parametric speech synthesis using deep gaussian processes. IEEE ACM Trans Audio Speech Lang Process 27(5):948–959

    Article  Google Scholar 

  • Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst 2:1097–1105

    Google Scholar 

  • Liu CL, Yin F, Wang DH, Wang Q F (2011) CASIA online and offline Chinese handwriting databases, Proc Int Conf Doc Anal Recognit, Beijing, China, September, pp 37–41 https://doi.org/10.1109/ICDAR.2011.17

  • López-Pérez M, García L, Benítez C, Molina R (2020) A contribution to deep learning approaches for automatic classification of volcano-seismic events: deep gaussian processes. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2020.3022995

    Article  Google Scholar 

  • Melnyk P, You ZQ, Li KQA (2020) high-performance CNN method for offline handwritten Chinese character recognition and visualization. Soft Comput 24(11):7977–7987

    Article  Google Scholar 

  • Mesquita DPP, Freitas LA, Gomes JPP, Mattos CLC (2020) LS-SVR as a bayesian RBF network. IEEE Trans Neural Netw Learn Syst 31(10):4389–4393

    Article  MathSciNet  Google Scholar 

  • Narang SR, Jindal MK (2020) On the recognition of Devanagari ancient handwritten characters using SIFT and Gabor features. Soft Comput 24(22):17279–17289

    Article  Google Scholar 

  • Nguyen TNA, Phung SL, Bouzerdoum A (2020) Hybrid deep learning-gaussian process network for pedestrian lane detection in unstructured scenes. IEEE Trans Neural Netw Learn Syst 31(12):5324–5338

    Article  Google Scholar 

  • Noury Z, Rezaei M (2020) Deep-CAPTCHA: a deep learning-based CAPTCHA solver for vulnerability assessment https://arxiv.org/abs/2006.08296

  • Raj M, Abirami S (2020) Structural representation-based off-line Tamil handwritten character recognition. Soft Comput 24(2):1447–1472

    Article  Google Scholar 

  • Redmon J, Farhadi A (2018) YOLOv3: an Incremental Improvement arXiv:1804.02767

  • Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L (2018) MobileNetV2: inverted residuals and linear bottlenecks. Proc IEEE Comput Soc Conf Comput Vision Pattern Recognit 8578572:4510–4520

    Google Scholar 

  • Sdiri B, Kaaniche M, Cheikh FA, Beghdadi A, Elle OJ (2019) Efficient enhancement of stereo endoscopic images based on joint wavelet decomposition and binocular combination. IEEE Trans Med Imaging 38(1):33–45

    Article  Google Scholar 

  • Shutin D, Buchgraber T, Kulkarni SR, Poor HV (2011) Fast variational sparse bayesian learning with automatic relevance determination for superimposed signals. IEEE Trans Signal Process 59(12):6257–6261

    Article  MathSciNet  MATH  Google Scholar 

  • Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. Int Conf Learn Represent, ICLR - Conf. Track Proc. arXiv:1409.1556

  • Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2015) Rethinking the inception architecture for computer vision. IEEE Comput Soc Conf Comput Vision Pattern Recognit. https://doi.org/10.1109/CVPR.2016.308

    Article  Google Scholar 

  • Tan M, Le QV (2019) MixConv: mixed depthwise convolutional Kernels. Br Mach Vis Conf. https://doi.org/10.1109/CVPR42600.2020.00165

    Article  Google Scholar 

  • Tan M, Le QV (2019b) EfficientNet: rethinking model scaling for convolutional neural networks. Int Conf Mach Learn 156104:10691–10700

    Google Scholar 

  • Titsias MK (2009) Variational learning of inducing variables in sparse Gaussian processes. J Mach Learn Res 5:567–574

    Google Scholar 

  • Wang J, Sun K, Cheng T, Jiang B, Zhao CD, Liu YD, Mu Y, Tan M, Wang X, Liu W, Xiao B (2020b) Deep high-resolution representation learning for visual recognition. IEEE Trans Pattern Anal Mach Intell 43(10):3349–3364

    Article  Google Scholar 

  • Wang C, Wu Y, Chen P, Hsieh J and Yeh I (2020a) CSPNet: a new backbone that can enhance learning capability of CNN, IEEE Comput. Soc Conf Comput Vis Pattern Recogn, Workshops pp 1571–1580

  • Xu TB, Yang PP, Zhang XY, Liu CL (2019) LightweightNet: Toward fast and lightweight convolutional neural networks via architecture distillation. Patt Recognit 8:272–284

    Article  Google Scholar 

  • Yang Q, Yan P, Zhang Y (2020) Low-dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE T Med Imaging 37(6):1348–1357

    Article  Google Scholar 

  • Yu D, Zhu Z, Min J (2020) Multi-scale decomposition enhancement algorithm for surface defect images of si3n4 ceramic bearing balls based on stationary wavelet transform. Adv Appl Ceram 120(1):47–57

    Article  Google Scholar 

  • Zhang XY, Bengiob Y, Liu CL (2017) Online and offline handwritten Chinese character recognition: a comprehensive study and new benchmark. Pattern Recognit 61:348–360

    Article  Google Scholar 

  • Zhang XY, Yin F, Zhang YM (2018) Drawing and recognizing chinese characters with recurrent neural network. IEEE Trans Pattern Anal Mach Intell 40(4):849–862

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by a project funded by the National Social Science Fund of China (No. 21VJXG043). All support is gratefully acknowledged.

Funding

This work was supported by the National Social Science Fund of China (No. 21VJXG043).

Author information

Authors and Affiliations

  1. College of Computer Science and Engineering, Dalian Minzu University, Dalian, 116600, China

    Hai Guo, Yifan Liu, Jingying Zhao & Yifan Song

  2. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China

    Jingying Zhao

Authors
  1. Hai Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yifan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jingying Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yifan Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

HG, YL, JZ, and YS made equal contributions regarding the conception of the work, the experimental work, the data analysis, and writing the paper.

Corresponding author

Correspondence to Hai Guo.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest.

Human and animals rights

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

Informed consent

All referred studies are highlighted in the literature review.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, H., Liu, Y., Zhao, J. et al. Offline handwritten Tai Le character recognition using wavelet deep convolution features and ensemble deep variationally sparse Gaussian processes. Soft Comput 27, 12439–12455 (2023). https://doi.org/10.1007/s00500-023-07883-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-023-07883-w

Keywords

Search

Navigation