Ancient Character Recognition: A Novel Image Dataset of Shui Manuscript Characters and Classification Model

TANG Minli; XIE Shaomin; LIU Xiangrong

doi:10.23919/cje.2022.00.077

Volume 32 Issue 1

Jan. 2023

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2023 > 32(1): 64-75

TANG Minli, XIE Shaomin, LIU Xiangrong. Ancient Character Recognition: A Novel Image Dataset of Shui Manuscript Characters and Classification Model[J]. Chinese Journal of Electronics, 2023, 32(1): 64-75. doi: 10.23919/cje.2022.00.077

Citation:

TANG Minli, XIE Shaomin, LIU Xiangrong. Ancient Character Recognition: A Novel Image Dataset of Shui Manuscript Characters and Classification Model[J]. Chinese Journal of Electronics, 2023, 32(1): 64-75. doi: 10.23919/cje.2022.00.077

Citation:

PDF( 4396 KB)

Ancient Character Recognition: A Novel Image Dataset of Shui Manuscript Characters and Classification Model

doi: 10.23919/cje.2022.00.077

TANG Minli^{1, 2, 3
,},
XIE Shaomin^{1, 3
,},
LIU Xiangrong^{1, 3
,}

1.
Department of Computer Science and Technology, School of Informatics, Xiamen University, Xiamen 361005, China
2.
School of Big Data Engineering, KaiLi University, KaiLi 556011, China
3.
Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan, Ministry of Culture and Tourism, Xiamen University, Xiamen 361005, China

More Information

Author Bio:
Minli TANG was born in Guizhou Province, China, in 1982. She is an Associate Professor at KaiLi University. She is currently studying for her Ph.D. degree at Xiamen University. Her research interests include computational intelligence, pattern recognition, and computer vision. (Email: tangml@stu.xmu.edu.cn)

Shaomin XIE was born in Guangdong Province, China, in 1998. He is studying for a master’s degree at Xiamen University. His research interests include computational intelligence and pattern recognition. (Email: xsmin@stu.xmu.edu.cn)

Xiangrong LIU (corresponding author) was born in Hunan Province, China, in 1978. He is a Professor and Ph.D. Supervisor at Xiamen University. His research interests include computational intelligence, data mining, and computational theory. (Email: xrliu@xmu.edu.cn)
Received Date: 2022-04-07
Accepted Date: 2022-07-05

Available Online: 2022-07-13

Publish Date: 2023-01-05

Abstract

Abstract

Shui manuscripts are part of the national intangible cultural heritage of China. Owing to the particularity of text reading, the level of informatization and intelligence in the protection of Shui manuscript culture is not adequate. To address this issue, this study created Shuishu_C, the largest image dataset of Shui manuscript characters that has been reported. Furthermore, after extensive experimental validation, we proposed ShuiNet-A, a lightweight artificial neural network model based on the attention mechanism, which combines channel and spatial dimensions to extract key features and finally recognize Shui manuscript characters. The effectiveness and stability of ShuiNet-A were verified through multiple sets of experiments. Our results showed that, on the Shui manuscript dataset with 113 categories, the accuracy of ShuiNet-A was 99.8%, which is 1.5% higher than those of similar studies. The proposed model could contribute to the classification accuracy and protection of ancient Shui manuscript characters.
- Shui manuscript characters,
- ShuiNet-A,
- Artificial neural network,
- Handwritten character recognition

FullText(HTML)

References(42)

References

[1]	T. Y. Wang, M. Endo, Y. Ohno, et al., “Convolutional neural network-based computer-aided diagnosis in Hiesho (cold sensation),” Computers in Biology and Medicine, vol.145, article no.105411, 2022. doi: 10.1016/j.compbiomed.2022.105411
[2]	S. Zou, C. Li, H. Sun, et al., “TOD-CNN: An effective convolutional neural network for tiny object detection in sperm videos,” Computers in Biology and Medicine, vol.146, article no.105543, 2022. doi: 10.1016/j.compbiomed.2022.105543
[3]	P. S. Glaret and P. Muthukannan, “Optimized convolution neural network based multiple eye disease detection,” Computers in Biology and Medicine, vol.146, article no.105648, 2022. doi: 10.1016/j.compbiomed.2022.105648
[4]	H. Shin, “Deep convolutional neural network-based signal quality assessment for photoplethysmogram,” Computers in Biology and Medicine, vol.145, article no.105430, 2022. doi: 10.1016/j.compbiomed.2022.105430
[5]	M. Sornam and C. V. Priya. “Deep convolutional neural network for handwritten tamil character recognition using principal component analysis,” in Proceedings of International Conference on Next Generation Computing Technologies, Springer, Singapore, pp.778–787, 2018.
[6]	M. A. Pragathi, K. Priyadarshini, S. Saveetha, et al., “Handwritten tamil character recognition using deep learning,” in Proceedings of IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), Vellore, India, pp.1–5,2019.
[7]	N. Ulaganathan, J. Rohith, A. S. Abhinav, et al., “Isolated handwritten Tamil character recognition using convolutional neural networks,” in Proceedings of IEEE 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India, pp.383–390, 2020.
[8]	C. Vinotheni, S. L. Pandian, and G. Lakshmi, “Modified convolutional neural network of Tamil character recognition,” in Advances in Distributed Computing and Machine Learning, Lecture Notes in Networks and Systems, vol.127, Springer, Singapore, pp.469–480, 2021.
[9]	R. Jayakanthan, A.H. Kumar, N. Sankarram, et al., “Handwritten Tamil character recognition using ResNet,” International Journal of Research in Engineering, Science and Management, vol.3, no.3, pp.133–137, 2020.
[10]	C. Boufenar, A. Kerboua, and M. Batouche, “Investigation on deep learning for off-line handwritten Arabic character recognition,” Cognitive Systems Research, vol.50, pp.180–195, 2018. doi: 10.1016/j.cogsys.2017.11.002
[11]	M. Elkhayati and Y. Elkettani, “Towards directing convolutional neural networks using computational geometry algorithms: Application to handwritten Arabic character recognition,” Advances in Science, Technology and Engineering Systems Journal, vol.5, no.5, pp.137–147, 2020. doi: 10.25046/aj050519
[12]	A. Qaroush, A. Awad, M. Modallal, et al., “Segmentation-based, omnifont printed Arabic character recognition without font identification,” Journal of King Saud University-Computer and Information Sciences, vol.34, no.6, pp.3025–3039, 2022. doi: 10.1016/j.jksuci.2020.10.001
[13]	F. Sarvaramini, A. Nasrollahzadeh, and M.Soryani, “Persian handwritten character recognition using convolutional neural network,” in Proceedings of IEEE Iranian Conference on Electrical Engineering, Mashhad, Iran, pp.1676–1680, 2018.
[14]	M. Rahmati, M. Fateh, M. Rezvani, et al., “Printed Persian OCR system using deep learning,” IET Image Processing, vol.14, no.15, pp.3920–3931, 2020. doi: 10.1049/iet-ipr.2019.0728
[15]	A. M. M. H and A. Bossaghzadeh, “Improving persian digit recognition by combining deep neural networks and SVM and using PCA,” in Proceedings of IEEE International Conference on Machine Vision and Image Processing (MVIP), Tehran, Iran, pp.1–5, 2020.
[16]	M. Jangid and S. Srivastava, “Handwritten devanagari character recognition using layer-wise training of deep convolutional neural networks and adaptive gradient methods,” Journal of Imaging, vol.4, no.2, article no.41, 2018. doi: 10.3390/jimaging4020041
[17]	R. Guha, N. Das, M. Kundu, et al., “DevNet: An efficient cnn architecture for handwritten devanagari character recognition,” International Journal of Pattern Recognition and Artificial Intelligence, vol.34, no.12, article no.2052009, 2020. doi: 10.1142/S0218001420520096
[18]	W. Wang, J. Zhang, J. Du, et al., “DenseRAN for offline handwritten chinese character recognition,” in Proceedings of IEEE 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), Niagara Falls, NY, USA, pp.104–109, 2018.
[19]	M. S. Yousef, K. F. Hussain, and U. S. Mohammed, “Accurate, data-efficient, unconstrained text recognition with convolutional neural networks,” Pattern Recognition, vol.108, article no.107482, 2020. doi: 10.1016/j.patcog.2020.107482
[20]	P. Melnyk, Z. You, and K. Li, “A high-performance CNN method for offline handwritten Chinese character recognition and visualization,” Soft Computing, vol.24, no.11, pp.7977–7987, 2020. doi: 10.1007/s00500-019-04083-3
[21]	A. Hazra, P. Choudhary, S. Inunganbi, et al., “Bangla-Meitei Mayek scripts handwritten character recognition using convolutional neural network,” Applied Intelligence, vol.51, no.4, pp.2291–2311, 2021. doi: 10.1007/s10489-020-01901-2
[22]	Y. A. Nanehkaran, D. Zhang, S. Salimi, et al., “Analysis and comparison of machine learning classifiers and deep neural networks techniques for recognition of Farsi handwritten digits,” The Journal of Supercomputing, vol.77, no.4, pp.3193–3222, 2021. doi: 10.1007/s11227-020-03388-7
[23]	D. X. Zhao and C. X. Zhao, “Feature extraction of offline handwritten Tibetan script,” Gansu Science and Technology, vol.24, no.5, pp.48–49, 2008. (in Chinese)
[24]	L. H. Zhu, “Offline handwritten Yi character recognition based on combination features and multi-classifier integration,” Journal of Yunnan Nationalities University: Natural Science Edition, vol.19, no.5, pp.329–333, 2010. (in Chinese)
[25]	H. Chun, “Research of printed Mongolian character recognition,” Journal of Inner Mongolia Minzu University (Natural Sciences), vol.29, no.06, pp.627–628, 2014. (in Chinese)
[26]	D. E. J. Fan, G. L. Gao, and H. J. Wu, “MHW Mongolian offline handwriting database and its application,” Journal of Chinese Information Processing, vol.32, no.1, pp.89–95, 2018. (in Chinese)
[27]	Y. Weng and C. Xia, “A new deep learning-based handwritten character recognition system on mobile computing devices,” Mobile Networks and Applications, vol.25, no.2, pp.402–411, 2020. doi: 10.1007/s11036-019-01243-5
[28]	C. L. Xia, “Research and application of water book image recognition algorithm based on deep learning,” Ph.D.Thesis, Central University for Nationalities, China, pp.26–41, 2019. (in Chinese)
[29]	H. Zhao, H. Chu, Y. Zhang,et al., “Improvement of ancient shui character recognition model based on convolutional neural network,” IEEE Access, vol.8, pp.33080–33087, 2020. doi: 10.1109/ACCESS.2020.2972807
[30]	Q. Ding, “Research on the feature extraction and classification method of Shui script under the Matlab platform,” Electronic Technology and Software Engineering, vol.14, pp.155–157, 2020. (in Chinese)
[31]	X. Z. Yang, S. Wu, H. Xia, et al., “Research on Shui characters extraction and recognition based on adaptive image enhancement technology,” Computer Science, vol.48, no.6A, pp.74–79, 2021. (in Chinese)
[32]	Y. LeCun, L. Bottou, Y. Bengio, et al., “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol.86, no.11, pp.2278–2324, 1998. doi: 10.1109/5.726791
[33]	A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Communications of the ACM, vol.60, no.6, pp.84–90, 2017. doi: 10.1145/3065386
[34]	K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint, arXiv:1409.1556, 2014.
[35]	K. He, X. Zhang, S. Ren, et al., “Deep residual learning for image recognition,” IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp.770–778, 2016.
[36]	D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” arXiv preprint, arXiv:1409.0473, 2014.
[37]	S. Woo, J. Park, J. Y. Lee, et al., “CBAM: Convolutional block attention module,” in Proceedings of European Conference on Computer Vision (ECCV), Munich, Germany, pp.3–19, 2018.
[38]	J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, pp.7132–7141, 2018.
[39]	X. Li, W. Wang, X. Hu, et al., “Selective kernel networks,” in Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, pp.510–519, 2019.
[40]	Q. Hou, D. Zhou, and J. Feng, “Coordinate attention for efficient mobile network design,” in Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, pp.13713–13722, 2021.
[41]	D. Misra, T. Nalamada, A. U. Arasanipalai, et al, “Rotate to attend: Convolutional triplet attention module,” in Proceedings of IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA, pp.3139–3148, 2021.
[42]	Z. Liu, Y. Lin, Y. Cao, et al., “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of IEEE/CVF International Conference on Computer Vision, Montreal, Canada, pp.9992–10002, 2021.