Convolutional Swin Encoder
This paper focuses on developing a deep learning architecture capable of identifying writers' attributes from their handwriting. It introduces Convolutional Swin Encoder (CSE), a novel architecture combining Visual Geometry Group Network (VGGNet) and Swin Transformer blocks. CSE is designed to...
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2025-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Subjects: | |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/138949 |
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| _version_ | 1850138045990305792 |
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| author | Aditya Majithia Arthur Paul Pedersen Michael Grossberg |
| author_facet | Aditya Majithia Arthur Paul Pedersen Michael Grossberg |
| author_sort | Aditya Majithia |
| collection | DOAJ |
| description |
This paper focuses on developing a deep learning architecture capable of identifying writers' attributes from their handwriting. It introduces Convolutional Swin Encoder (CSE), a novel architecture combining Visual Geometry Group Network (VGGNet) and Swin Transformer blocks. CSE is designed to handle multi-label classification using images of individual handwritten words. As a unified encoder, it can predict writers' attributes such as identity, gender, age, and handedness. Using a word-level segmentation approach, CSE achieves competitive performance compared to page-level methods, which typically rely on separate classifiers instead of a unified one.
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| format | Article |
| id | doaj-art-42b68ab9b87841db8424ae76b45e627c |
| institution | OA Journals |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-42b68ab9b87841db8424ae76b45e627c2025-08-20T02:30:39ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622025-05-0138110.32473/flairs.38.1.138949Convolutional Swin EncoderAditya Majithia0Arthur Paul Pedersen1https://orcid.org/0000-0002-2164-6404Michael Grossberg2City College of New YorkThe City University of New York (CUNY)The City University of New York (CUNY) This paper focuses on developing a deep learning architecture capable of identifying writers' attributes from their handwriting. It introduces Convolutional Swin Encoder (CSE), a novel architecture combining Visual Geometry Group Network (VGGNet) and Swin Transformer blocks. CSE is designed to handle multi-label classification using images of individual handwritten words. As a unified encoder, it can predict writers' attributes such as identity, gender, age, and handedness. Using a word-level segmentation approach, CSE achieves competitive performance compared to page-level methods, which typically rely on separate classifiers instead of a unified one. https://journals.flvc.org/FLAIRS/article/view/138949Authorship AttributionHandwriting AnalysisSwin Transformersmultiple task learning |
| spellingShingle | Aditya Majithia Arthur Paul Pedersen Michael Grossberg Convolutional Swin Encoder Proceedings of the International Florida Artificial Intelligence Research Society Conference Authorship Attribution Handwriting Analysis Swin Transformers multiple task learning |
| title | Convolutional Swin Encoder |
| title_full | Convolutional Swin Encoder |
| title_fullStr | Convolutional Swin Encoder |
| title_full_unstemmed | Convolutional Swin Encoder |
| title_short | Convolutional Swin Encoder |
| title_sort | convolutional swin encoder |
| topic | Authorship Attribution Handwriting Analysis Swin Transformers multiple task learning |
| url | https://journals.flvc.org/FLAIRS/article/view/138949 |
| work_keys_str_mv | AT adityamajithia convolutionalswinencoder AT arthurpaulpedersen convolutionalswinencoder AT michaelgrossberg convolutionalswinencoder |