The application research of ancient calligraphy recognition model based on improved filtering and AI technology in calligraphy education in colleges and universities
Abstract With the increase of Chinese emphasis on calligraphy education, more and more students choose to learn calligraphy courses in colleges. However, the present process of calligraphy education lacks enough content of appreciation and learning of stele characters of ancient calligraphers. To re...
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| Format: | Article |
| Language: | English |
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Springer
2025-02-01
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| Series: | Discover Artificial Intelligence |
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| Online Access: | https://doi.org/10.1007/s44163-025-00223-x |
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| author | Xing You |
| author_facet | Xing You |
| author_sort | Xing You |
| collection | DOAJ |
| description | Abstract With the increase of Chinese emphasis on calligraphy education, more and more students choose to learn calligraphy courses in colleges. However, the present process of calligraphy education lacks enough content of appreciation and learning of stele characters of ancient calligraphers. To reduce the difficulty of carrying out the content of stele calligraphy education in colleges and universities, this study constructed a recognition model of ancient Chinese characters in stele. The recognition model will filter and denoise the input image data, open and close calculation, binarization, data amplification and skeletonization. It feeds into two designed convolutional neural networks to extract graphic features. The test results of recognition models showed that the recognition accuracy and recall rates of SCF, Gabor, Fast R-CNN and S-ICNN models on the test set were 79.13%, 74.52%, 82.66%, 93.87% and 76.94%, 72.41%, 88.25%, 94.09%, respectively. The data showed that the recognition accuracy of the ancient Chinese character recognition model designed by this research is significantly higher than the common methods in the market. It has certain application potential, but further research is needed to realize the purpose of applying it in college calligraphy education. |
| format | Article |
| id | doaj-art-153e21e0e35946368ed8597ce05837ad |
| institution | Kabale University |
| issn | 2731-0809 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-153e21e0e35946368ed8597ce05837ad2025-02-09T12:46:37ZengSpringerDiscover Artificial Intelligence2731-08092025-02-015111310.1007/s44163-025-00223-xThe application research of ancient calligraphy recognition model based on improved filtering and AI technology in calligraphy education in colleges and universitiesXing You0Calligraphy College, Hebei Academy of Fine ArtsAbstract With the increase of Chinese emphasis on calligraphy education, more and more students choose to learn calligraphy courses in colleges. However, the present process of calligraphy education lacks enough content of appreciation and learning of stele characters of ancient calligraphers. To reduce the difficulty of carrying out the content of stele calligraphy education in colleges and universities, this study constructed a recognition model of ancient Chinese characters in stele. The recognition model will filter and denoise the input image data, open and close calculation, binarization, data amplification and skeletonization. It feeds into two designed convolutional neural networks to extract graphic features. The test results of recognition models showed that the recognition accuracy and recall rates of SCF, Gabor, Fast R-CNN and S-ICNN models on the test set were 79.13%, 74.52%, 82.66%, 93.87% and 76.94%, 72.41%, 88.25%, 94.09%, respectively. The data showed that the recognition accuracy of the ancient Chinese character recognition model designed by this research is significantly higher than the common methods in the market. It has certain application potential, but further research is needed to realize the purpose of applying it in college calligraphy education.https://doi.org/10.1007/s44163-025-00223-xFilteringStele character recognitionSkeletonizationData amplificationConvolutional neural network |
| spellingShingle | Xing You The application research of ancient calligraphy recognition model based on improved filtering and AI technology in calligraphy education in colleges and universities Discover Artificial Intelligence Filtering Stele character recognition Skeletonization Data amplification Convolutional neural network |
| title | The application research of ancient calligraphy recognition model based on improved filtering and AI technology in calligraphy education in colleges and universities |
| title_full | The application research of ancient calligraphy recognition model based on improved filtering and AI technology in calligraphy education in colleges and universities |
| title_fullStr | The application research of ancient calligraphy recognition model based on improved filtering and AI technology in calligraphy education in colleges and universities |
| title_full_unstemmed | The application research of ancient calligraphy recognition model based on improved filtering and AI technology in calligraphy education in colleges and universities |
| title_short | The application research of ancient calligraphy recognition model based on improved filtering and AI technology in calligraphy education in colleges and universities |
| title_sort | application research of ancient calligraphy recognition model based on improved filtering and ai technology in calligraphy education in colleges and universities |
| topic | Filtering Stele character recognition Skeletonization Data amplification Convolutional neural network |
| url | https://doi.org/10.1007/s44163-025-00223-x |
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