[1]XIONG Tieniu,QIU Jifang,HU Jian.Collection and sorting method of ancient Yi character images based on deep learning technology[J].CAAI Transactions on Intelligent Systems,2025,20(4):928-935.[doi:10.11992/tis.202406036]
Copy

Collection and sorting method of ancient Yi character images based on deep learning technology

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 4 Page number: 928-935 Column: 学术论文—机器学习 Public date: 2025-08-05
Title:
Collection and sorting method of ancient Yi character images based on deep learning technology
Author(s):
XIONG Tieniu1; 2;  QIU Jifang3;  HU Jian1; 2
1. The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, Southwest Minzu University, Chengdu 610225, China;
2. College of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu 610225, China;
3. School of Chinese Language and Literature, Southwest Minzu University, Chengdu 610225, China
Keywords:
deep learning; ancient Yi characters; ancient literatures; image processing; similarity matching; feature extraction; object detection; digitalization
CLC:
TP391.4; TP391.1
DOI:
10.11992/tis.202406036
Abstract:
The ancient Yi script is one of the important carriers of Chinese culture. However, manually collecting and organizing a large amount of ancient Yi script is time-consuming and labor-intensive. Additionally, very few people can recognize ancient Yi script, and their numbers are dwindling, which makes the task even more difficult. In response to this, this paper proposes a new approach to collecting and organizing images of the ancient Yi script based on deep learning technology. For image collection, the object detection model is used to locate each ancient Yi character in the images of ancient Yi manuscripts, and the characters are extracted from these images accordingly. For image organization, because modern standardized Yi characters are derived from ancient Yi characters, standardized Yi character font files are used to generate images of the Yi characters automatically to construct a dataset. This dataset is then used to train an algorithm for extracting features of ancient Yi script images, which effectively addresses the current lack of an ancient Yi script image dataset due to the large number of characters, many variants, and incomplete organization. Subsequently, matching the features of the collected ancient Yi script images with those of already cataloged images enables determining whether the collected images have been previously recorded and thereby organizing uncatalogued ancient Yi script images. Experiments conducted with various typical feature extraction algorithms and similarity computation methods validate the effectiveness of this approach.
References:
[1] 孔祥卿. 彝文的源流[M]. 北京: 民族出版社, 2005.
[2] 韩旭. 彝文古籍字符检测和识别的研究与实现[D]. 重庆: 西南大学, 2020.
HAN Xu. Research and implementation of character detection and recognition in Yi ancient books[D]. Chongqing: Southwest University, 2020.
[3] 贾晓栋. 基于深度学习的手写彝文识别技术应用研究[D]. 北京: 中央民族大学, 2017.
JIA Xiaodong. Research on the application of handwritten Yi recognition technology based on deep learning[D]. Beijing: Central University for Nationalities, 2017.
[4] 胡峰, 李路正, 代劲, 等. 结合聚类边界采样的主动学习[J]. 智能系统学报, 2024, 19(2): 482-492.
HU Feng, LI Luzheng, DAI Jin, et al. Active learning combined with clustering boundary sampling[J]. CAAI transactions on intelligent systems, 2024, 19(2): 482-492.
[5] 王定旺. 彝文联机手写体识别的研究与应用[D]. 重庆: 西南大学, 2021.
WANG Dingwang. Research and application of online handwriting recognition in Yi language[D]. Chongqing: Southwest University, 2021.
[6] WANG Chongguang, EVANS K, HARTLEY D, et al. A systematic review of artificial neural network techniques for analysis of foot plantar pressure[J]. Biocybernetics and biomedical engineering, 2024, 44(1): 197-208.
[7] 陈善雄, 韩旭, 林小渝, 等. 基于MSER和CNN的彝文古籍文献的字符检测方法[J]. 华南理工大学学报(自然科学版), 2020, 48(6): 123-133.
CHEN Shanxiong, HAN Xu, LIN Xiaoyu, et al. MSER and CNN-based method for character detection in ancient Yi books[J]. Journal of South China University of Technology (natural science edition), 2020, 48(6): 123-133.
[8] CHEN Huizhong, TSAI S S, SCHROTH G, et al. Robust text detection in natural images with edge-enhanced Maximally Stable Extremal Regions[C]//2011 18th IEEE International Conference on Image Processing. Brussels: IEEE, 2011: 2609-2612.
[9] 陈善雄, 王小龙, 韩旭, 等. 一种基于深度学习的古彝文识别方法[J]. 浙江大学学报(理学版), 2019, 46(3): 261-269.
CHEN Shanxiong, WANG Xiaolong, HAN Xu, et al. A recognition method of ancient Yi character based on deep learning[J]. Journal of Zhejiang University (science edition), 2019, 46(3): 261-269.
[10] CICHOCKI A, CRUCES S, AMARI S I. Generalized alpha-beta divergences and their application to robust nonnegative matrix factorization[J]. Entropy, 2011, 13(1): 134-170.
[11] 贵州省彝学研究会. 西南彝志[M]. 贵阳: 贵州民族出版社, 2015.
[12] 《彝文典籍集成》编委会. 彝文典籍集成·四川卷·教育[M]. 成都: 四川民族出版社, 2014.
[13] 滇川黔桂彝文协作组编. 滇川黔桂彝文字集[M]. 昆明: 云南民族出版社, 2004.
[14] JIANG Peiyuan, ERGU Daji, LIU Fangyao, et al. A review of yolo algorithm developments[J]. Procedia computer science, 2022, 199: 1066-1073.
[15] 沙马拉毅. 《规范彝文方案》推行30年实践效果述评[J]. 西南民族大学学报(人文社科版), 2010, 31(8): 28-31.
SHA M. A review of the practice effect of standardizing Yi language program for 30 years[J]. Journal of Southwest University for Nationalities (humanities and social science edition), 2010, 31(8): 28-31.
[16] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(6): 1137-1149.
[17] FARHADI A, REDMON J. Yolov3: An incremental improvement[C]//Computer vision and pattern recognition. Berlin: Springer, 2018, 1804: 1-6.
[18] JANI M, FAYYAD J, AL-YOUNES Y, et al. Model compression methods for YOLOv5: a review[EB/OL]. (2023-07-21)[2024-06-21]. https://arxiv.org/abs/2307.11904v1.
[19] WANG Ao, CHEN Hui, LIU Lihao, et al. YOLOv10: real-time end-to-end object detection[EB/OL]. (2024-05-23)[2024-06-21]. https://arxiv.org/abs/2405.14458v2.
[20] TIAN Zhi, SHEN Chunhua, CHEN Hao, et al. FCOS: fully convolutional one-stage object detection[EB/OL]. (2019-04-02)[2024-06-21]. https://arxiv.org/abs/1904.01355v5.
[21] ZHANG T Y, SUEN C Y. A fast parallel algorithm for thinning digital patterns[J]. Communications of the acm, 1984, 27(3): 236-239.
[22] YADAV S, SAWALE M D. A review on image classification using deep learning[J]. World journal of advanced research and reviews, 2023, 17(1): 480-482.
[23] ZHAO Xia, WANG Limin, ZHANG Yufei, et al. A review of convolutional neural networks in computer vision[J]. Artificial intelligence review, 2024, 57(4): 99.
[24] SRIVASTAVA D, WADHVANI R, GYANCHANDANI M, et al. A review: color feature extraction methods for content based image retrieval[J]. International journal of computational engineering & management, 2015, 18(3): 9-13.
[25] DENG Jia, DONG Wei, SOCHER R, et al. ImageNet: a large-scale hierarchical image database[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami: IEEE, 2009: 248-255.
[26] 曾婷, 唐孝, 谭阳, 等. 相似度三支决策模糊粗糙集模型的决策代价研究[J]. 智能系统学报, 2020, 15(6): 1068-1078.
ZENG Ting, TANG Xiao, TAN Yang, et al. Decision costs of the similarity three-way decision-theoretic fuzzy rough set model[J]. CAAI transactions on intelligent systems, 2020, 15(6): 1068-1078.
[27] 卞则康, 王士同. 基于混合距离学习的鲁棒的模糊C均值聚类算法[J]. 智能系统学报, 2017, 12(4): 450-458.
BIAN Zekang, WANG Shitong. Robust FCM clustering algorithm based on hybrid-distance learning[J]. CAAI transactions on intelligent systems, 2017, 12(4): 450-458.
[28] LEI Shiye, TAO Dacheng. A comprehensive survey of dataset distillation[J]. IEEE transactions on pattern analysis and machine intelligence, 2024, 46(1): 17-32.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems