Definition and Automatic Extraction Performance Analysis of Stroke Elements in the English Alphabet
Fonts are a critical element that determines the perception of any medium. To ensure consistent and culturally appropriate font selection across diverse language groups, a multilingual font matching system is currently in development. This research focuses on leveraging the latest advancements in ma...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10418082/ |
| _version_ | 1797319582194597888 |
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| author | Soon-Bum Lim Yujin Lee Yoojeong Song |
| author_facet | Soon-Bum Lim Yujin Lee Yoojeong Song |
| author_sort | Soon-Bum Lim |
| collection | DOAJ |
| description | Fonts are a critical element that determines the perception of any medium. To ensure consistent and culturally appropriate font selection across diverse language groups, a multilingual font matching system is currently in development. This research focuses on leveraging the latest advancements in machine learning and computer vision to deeply understand font characteristics and enhance the accuracy of multilingual font matching. Utilizing the ‘stroke elements’ of fonts is crucial for this matching, building upon the successful development of a method to calculate similarity between Korean fonts in previous studies. We have applied this approach to the English alphabet, defining distinctive ‘stroke elements’ and developing a deep learning model for their automatic extraction. Additionally, we evaluate the performance of this stroke element extraction model and discuss strategies to further improve extraction accuracy. This groundwork establishes the basis for multilingual font matching and enables the recommendation of similar fonts using the ‘stroke elements’ of the English alphabet. |
| first_indexed | 2024-03-08T04:09:04Z |
| format | Article |
| id | doaj.art-db8211e420d940cb84b0b72a1d886b16 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T04:09:04Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-db8211e420d940cb84b0b72a1d886b162024-02-09T00:03:10ZengIEEEIEEE Access2169-35362024-01-0112189311893810.1109/ACCESS.2024.336048210418082Definition and Automatic Extraction Performance Analysis of Stroke Elements in the English AlphabetSoon-Bum Lim0Yujin Lee1Yoojeong Song2https://orcid.org/0000-0003-1666-6803Department of IT Engineering, Sookmyung Women’s University, Seoul, South KoreaDepartment of IT Engineering, Sookmyung Women’s University, Seoul, South KoreaSchool of Computer Science, Semyung University, Jecheon, South KoreaFonts are a critical element that determines the perception of any medium. To ensure consistent and culturally appropriate font selection across diverse language groups, a multilingual font matching system is currently in development. This research focuses on leveraging the latest advancements in machine learning and computer vision to deeply understand font characteristics and enhance the accuracy of multilingual font matching. Utilizing the ‘stroke elements’ of fonts is crucial for this matching, building upon the successful development of a method to calculate similarity between Korean fonts in previous studies. We have applied this approach to the English alphabet, defining distinctive ‘stroke elements’ and developing a deep learning model for their automatic extraction. Additionally, we evaluate the performance of this stroke element extraction model and discuss strategies to further improve extraction accuracy. This groundwork establishes the basis for multilingual font matching and enables the recommendation of similar fonts using the ‘stroke elements’ of the English alphabet.https://ieeexplore.ieee.org/document/10418082/Artificial neural networksdeep learningdiverse font stylesfontsobject extraction model |
| spellingShingle | Soon-Bum Lim Yujin Lee Yoojeong Song Definition and Automatic Extraction Performance Analysis of Stroke Elements in the English Alphabet IEEE Access Artificial neural networks deep learning diverse font styles fonts object extraction model |
| title | Definition and Automatic Extraction Performance Analysis of Stroke Elements in the English Alphabet |
| title_full | Definition and Automatic Extraction Performance Analysis of Stroke Elements in the English Alphabet |
| title_fullStr | Definition and Automatic Extraction Performance Analysis of Stroke Elements in the English Alphabet |
| title_full_unstemmed | Definition and Automatic Extraction Performance Analysis of Stroke Elements in the English Alphabet |
| title_short | Definition and Automatic Extraction Performance Analysis of Stroke Elements in the English Alphabet |
| title_sort | definition and automatic extraction performance analysis of stroke elements in the english alphabet |
| topic | Artificial neural networks deep learning diverse font styles fonts object extraction model |
| url | https://ieeexplore.ieee.org/document/10418082/ |
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