JACIII Vol.27 No.4 pp. 567-575
doi: 10.20965/jaciii.2023.p0567


Offline Handwritten Chinese Character Using Convolutional Neural Network: State-of-the-Art Methods

Yingna Zhong*, Kauthar Mohd Daud*,† ORCID Icon, Ain Najiha Binti Mohamad Nor*, Richard Adeyemi Ikuesan** ORCID Icon, and Kohbalan Moorthy*** ORCID Icon

*Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia
Bangi, Selangor 43600, Malaysia

Corresponding author

**Department of Computing and Applied Technology, College of Technological Innovation, Zayed University
Abu Dhabi 19282, United Arab Emirates

***Faculty of Computing, Universiti Malaysia Pahang
Pekan, Pahang 26600, Malaysia

October 19, 2022
March 7, 2023
July 20, 2023
handwritten Chinese characters recognition, convolutional neural network, filtering techniques, activation functions

Given the presence of handwritten documents in human transactions, including email sorting, bank checks, and automating procedures, handwritten characters recognition (HCR) of documents has been invaluable to society. Handwritten Chinese characters (HCC) can be divided into offline and online categories. Online HCC recognition (HCCR) involves the trajectory movement of the pen tip for expressing linguistic content. In contrast, offline HCCR involves analyzing and categorizing the sample binary or grayscale images of characters. As recognition technology develops, academics’ interest in Chinese character recognition has continuously increased, as it significantly affects social and economic development. Recent development in this area is promising. However, the recognition accuracy of offline HCCR is still a sophisticated challenge owing to their complexity and variety of writing styles. With the advancement of deep learning, convolutional neural network (CNN)-based algorithms have demonstrated distinct benefits in offline HCCR and have achieved outstanding results. In this review, we aim to show the different HCCR methods for tackling the complexity and variability of offline HCC writing styles. This paper also reviews different activation functions used in offline HCCR and provides valuable assistance to new researchers in offline Chinese handwriting recognition by providing a succinct study of various methods for recognizing offline HCC.

Cite this article as:
Y. Zhong, K. Daud, A. Nor, R. Ikuesan, and K. Moorthy, “Offline Handwritten Chinese Character Using Convolutional Neural Network: State-of-the-Art Methods,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.4, pp. 567-575, 2023.
Data files:
  1. [1] F. Ye and Z. Qin, “Research on pattern representation based on keyword and word embedding in Chinese entity relation extraction,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.4, pp. 475-482, 2018.
  2. [2] D. T. Long, “A facial expressions recognition method using residual network architecture for online learning evaluation,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.6, pp. 953-962, 2021.
  3. [3] R. Dai, C. Liu, and B. Xiao, “Chinese character recognition: History, status and prospects,” Front. Comput. Sci. China, Vol.1, No.2, pp. 126-136, 2007.
  4. [4] C.-L. Liu, S. Jaeger, and M. Nakagawa, “Online recognition of Chinese characters: The state-of-the-art,” IEEE Trans. Pattern Anal. Mach. Intell., Vol.26, No.2, pp. 198-213, 2004.
  5. [5] X.-Y. Zhang, Y. Bengio, and C.-L. Liu, “Online and offline handwritten Chinese character recognition: A comprehensive study and new benchmark,” Pattern Recognit., Vol.61, pp. 348-360, 2017.
  6. [6] T. Wakahara and K. Odaka, “On-line cursive Kanji character recognition using stroke-based affine transformation,” IEEE Trans. Pattern Anal. Mach. Intell., Vol.19, No.12, pp. 1381-1385, 1997.
  7. [7] J. Zheng, X. Ding, and Y. Wu, “Recognizing on-line handwritten Chinese character via FARG matching,” Proc. 4th Int. Conf. Doc. Anal. Recognit., Vol.2, pp. 621-624, 1997.
  8. [8] S. Aqab and M. U. Tariq, “Handwriting recognition using artificial intelligence neural network and image processing,” Int. J. Adv. Comput. Sci. Appl., Vol.11, No.7, 2020.
  9. [9] F. Kimura et al., “Modified quadratic discriminant functions and the application to Chinese character recognition,” IEEE Trans. Pattern Anal. Mach. Intell., Vol.9, No.1, pp. 149-153, 1987.
  10. [10] C.-L. Liu, H. Sako, and H. Fujisawa, “Discriminative learning quadratic discriminant function for handwriting recognition,” IEEE Trans. Neural Netw., Vol.15, No.2, pp. 430-444, 2004.
  11. [11] H. Zhang et al., “Three-dimensional unmanned aerial vehicle route planning using hybrid differential evolution,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.7, pp. 820-828, 2020.
  12. [12] Z. Zhong, L. Jin, and Z. Xie, “High performance offline handwritten Chinese character recognition using GoogLeNet and directional feature maps,” 2015 13th Int. Conf. Doc. Anal. Recogit. (ICDAR), pp. 846-850, 2015.
  13. [13] Y. Li et al., “Complementary convolution residual networks for semantic segmentation in street scenes with deep Gaussian CRF,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.1, pp. 3-12, 2021.
  14. [14] W. Liu, L. Ma, and M. Cui, “Learning-based stereoscopic view synthesis with cascaded deep neural networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.3, pp. 393-406, 2022.
  15. [15] T. Wakahara et al., “On-line cursive Kanji character recognition as stroke correspondence problem,” Proc. 3rd Int. Conf. Doc. Anal. Recognit., Vol.2, pp. 1059-1064, 1995.
  16. [16] W. Gu et al., “Stock prediction based on news text analysis,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.5, pp. 581-591, 2021.
  17. [17] J. G. Daugman, “Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression,” IEEE Trans. Acoust. Speech Signal Process., Vol.36, No.7, pp. 1169-1179, 1988.
  18. [18] Y. Ge, Q. Huo, and Z.-D. Feng, “Offline recognition of handwritten Chinese characters using Gabor features, CDHMM modeling and MCE training,” 2002 IEEE Int. Conf. Acoust. Speech Signal Process., pp. I-1053-I-1056, 2002.
  19. [19] M. Z. Alom et al., “Handwritten bangla digit recognition using deep learning,” arXiv: 1705.02680, 2017.
  20. [20] M. Shi et al., “Handwritten numeral recognition using gradient and curvature of gray scale image,” Pattern Recognit., Vol.35, No.10, pp. 2051-2059, 2002.
  21. [21] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” 2005 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Vol.1, pp. 886-893, 2005.
  22. [22] H. Liu and X. Ding, “Handwritten character recognition using gradient feature and quadratic classifier with multiple discrimination schemes,” 8th Int. Conf. Doc. Anal. Recognit. (ICDAR’05), Vol.1, pp. 19-23, 2005.
  23. [23] C.-L. Liu, “High accuracy handwritten Chinese character recognition using quadratic classifiers with discriminative feature extraction,” 18th Int. Conf. Pattern Recognit., pp. 942-945, 2006.
  24. [24] X. Wei, S. Lu, and Y. Lu, “Compact MQDF classifiers using sparse coding for handwritten Chinese character recognition,” Pattern Recognit., Vol.76, pp. 679-690, 2018.
  25. [25] L. Chen et al., “Beyond human recognition: A CNN-based framework for handwritten character recognition,” 2015 3rd IAPR Asian Conf. Pattern Recognit. (ACPR), pp. 695-699, 2015.
  26. [26] Z. Li et al., “Building efficient CNN architecture for offline handwritten Chinese character recognition,” Int. J. Doc. Anal. Recognit., Vol.21, No.4, pp. 233-240, 2018.
  27. [27] I. J. Goodfellow et al., “Multi-digit number recognition from street view imagery using deep convolutional neural networks,” arXiv: 1312.6082, 2013.
  28. [28] Y. Taigman et al., “DeepFace: Closing the gap to human-level performance in face verification,” 2014 IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 1701-1708, 2014.
  29. [29] B. Graham, “Spatially-sparse convolutional neural networks,” arXiv: 1409.6070, 2014.
  30. [30] X. Xiao et al., “Building fast and compact convolutional neural networks for offline handwritten Chinese character recognition,” Pattern Recognit., Vol.72, pp. 72-81, 2017.
  31. [31] P. Melnyk, Z. You, and K. Li, “A high-performance CNN method for offline handwritten Chinese character recognition and visualization,” Soft Comput., Vol.24, No.11, pp. 7977-7987, 2020.
  32. [32] J. Zou, J. Zhang, and L. Wang, “Handwritten Chinese character recognition by convolutional neural network and similarity ranking,” arXiv: 1908.11550, 2019.
  33. [33] N. Bi, J. Chen, and J. Tan, “The Handwritten Chinese Character Recognition Uses Convolutional Neural Networks with the GoogLeNet,” Int. J. of Pattern Recognition and Artificial Intelligence, Vol.33, No.11, Article No.1940016, 2019.
  34. [34] C. Szegedy et al., “Going deeper with convolutions,” 2015 IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 1-9, 2015.
  35. [35] J. Gan, W. Wang, and K. Lu, “Compressing the CNN architecture for in-air handwritten Chinese character recognition,” Pattern Recognit. Lett., Vol.129, pp. 190-197, 2020.
  36. [36] F. Min, S. Zhu, and Y. Wang, “Offline handwritten Chinese character recognition based on improved GoogLeNet,” Proc. 2020 3rd Int. Conf. Artif. Intell. Pattern Recognit. (AIPR’20), pp. 42-46, 2020.
  37. [37] L. Liu, Q. Wang, and Y. Li, “Improved Chinese sentence semantic similarity calculation method based on multi-feature fusion,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.4, pp. 442-449, 2021.
  38. [38] Z. Li et al., “Deep template matching for offline handwritten Chinese character recognition,” J. Eng., Vol.2020, No.4, pp. 120-124, 2020.
  39. [39] N. Aleskerova and A. Zhuravlev, “Handwritten Chinese characters recognition using two-stage hierarchical convolutional neural network,” 2020 17th Int. Conf. Front. Handwrit. Recognit. (ICFHR), pp. 343-348, 2020.
  40. [40] B. Liu, X. Xu, and Y. Zhang, “Offline handwritten Chinese text recognition with convolutional neural networks,” arXiv: 2006.15619, 2020.
  41. [41] Y. Wang et al., “A residual-attention offline handwritten Chinese text recognition based on fully convolutional neural networks,” IEEE Access, Vol.9, pp. 132301-132310, 2021.
  42. [42] C. Zhou et al., “POI classification method based on feature extension and deep learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.7, pp. 944-952, 2020.
  43. [43] J. Liu and Y. Li, “Visual servoing with deep learning and data augmentation for robotic manipulation,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.7, pp. 953-962, 2020.
  44. [44] R. Ameri et al., “Classification of handwritten Chinese numbers with convolutional neural networks,” 2021 5th Int. Conf. Pattern Recognit. Image Anal. (IPRIA), 2021.
  45. [45] F. N. Iandola et al., “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size,” arXiv: 1602.07360, 2014.
  46. [46] M. Sandler et al., “MobileNetV2: Inverted residuals and linear bottlenecks,” 2018 IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 4510-4520, 2018.
  47. [47] D. Kanda, S. Kawai, and H. Nobuhara, “Visualization method corresponding to regression problems and its application to deep learning-based gaze estimation model,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.5, pp. 676-684, 2020.
  48. [48] Y. Li and Y. Li, “Design and implementation of handwritten Chinese character recognition method based on CNN and TensorFlow,” 2021 IEEE Int. Conf. Artif. Intell. Comput. Appl. (ICAICA), pp. 878-882, 2021.
  49. [49] W. H. Liu, K. M. Lim, and C. P. Lee, “Visually similar handwritten Chinese character recognition with convolutional neural network,” 2021 9th Int. Conf. Inf. Commun. Technol. (ICoICT), pp. 175-179, 2021.
  50. [50] P. Patidar and S. Srivastava, “Image de-noising by various filters for different noise,” Int. J. Comput. Appl., Vol.9, No.4, pp. 45-50, 2010.
  51. [51] X. Xu et al., “A sophisticated offline network developed for recognizing handwritten Chinese character efficiently,” Comput. Electr. Eng., Vol.100, Article No.107857, 2022.
  52. [52] C. Zhang, D. Meng, and J. He, “VGG-16 convolutional neural network-oriented detection of filling flow status of viscous food,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.4, pp. 568-575, 2020.
  53. [53] P. P. Shetti and A. P. Patil, “Performance comparison of mean, median and Wiener filter in MRI image de-noising,” Int. J. Res. Trends Innov., Vol.2, No.6, pp. 371-375, 2017.
  54. [54] Sarita, R. Dass, and J. Saini, “Assessment of de-noising filters for brain MRI T1-weighted contrast-enhanced images,” Emergent Converging Technol. Biomed. Syst.: Sel. Proc. ETBS 2021, pp. 607-613, 2022.
  55. [55] R. Srinivas and S. Panda, “Performance analysis of various filters for image noise removal in different noise environment,” Int. J. Adv. Comput. Res., Vol.3, No.4, pp. 47-52, 2013.
  56. [56] M. C. Arya and A. Semwal, “Comparison on average, median and Wiener filter using lung images,” Int. Res. J. Eng. Technol., Vol.4, No.2, pp. 131-133, 2017.
  57. [57] Z.-H. Zhang et al., “Lenet-5 convolution neural network with Mish activation function and fixed memory step gradient descent method,” 2019 16th Int. Comput. Conf. Wavelet Act. Media Technol. Inf. Process., pp. 196-199, 2019.
  58. [58] H. Guo, L. Ai, and S. Chen, “Application of convolutional neural network in handwritten Chinese character recognition,” 2020 IEEE Int. Conf. on Inf. Technol. Data Artif. Intell. (ICIBA), pp. 1278-1281, 2020.
  59. [59] A. Maniatopoulos and N. Mitianoudis, “Learnable leaky ReLU (LeLeLU): An alternative accuracy-optimized activation function.” Information, Vol.12, No.12, Article No.513, 2021.
  60. [60] X. Liu and X. Di, “TanhExp: A smooth activation function with high convergence speed for lightweight neural networks,” IET Comput. Vis., Vol.15, No.2, pp. 136-150, 2020.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 05, 2024