Alphabet Handwriting Recognition: From Wood‐Framed Hydrogel Arrays Design to Machine Learning Decoding
Abstract Handwriting recognition is a highly integrated system, demanding hardware to collect handwriting signals and software to deal with input data. Nonetheless, the design of such a system from scratch with sustainable materials and an easily accessible computing network presents significant cha...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202404437 |
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| _version_ | 1846116847555969024 |
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| author | Guihua Yan Xichen Hu Ziyue Miao Yongde Liu Xianhai Zeng Lu Lin Olli Ikkala Bo Peng |
| author_facet | Guihua Yan Xichen Hu Ziyue Miao Yongde Liu Xianhai Zeng Lu Lin Olli Ikkala Bo Peng |
| author_sort | Guihua Yan |
| collection | DOAJ |
| description | Abstract Handwriting recognition is a highly integrated system, demanding hardware to collect handwriting signals and software to deal with input data. Nonetheless, the design of such a system from scratch with sustainable materials and an easily accessible computing network presents significant challenges. In pursuit of this goal, a flexible, and electrically conductive wood‐derived hydrogel array is developed as a handwriting input panel, enabling recognizing alphabet handwriting assisted by machine learning technique. For this, lignin extraction‐refill, polypyrrole coating, and polyacrylic acid filling, endowing flexibility, and electrical conduction to wood are sequentially implemented. Subsequently, these woods are manufactured into a 5 × 5 array, creating a matrix of signals upon handwriting. Efficient handwritten recognition is then achieved through appropriate manual feature extraction and algorithms with low complexity within a computing network, as demonstrated in this work, the strategic choice of expertise‐based feature engineering and simplified algorithms effectively boost the overall model performance on handwriting recognition. With potential adaptability, further applications in customized wearable devices and hands‐on healthcare appliances are envisioned. |
| format | Article |
| id | doaj-art-d51e11c1d134401f8b33f200fc0b3034 |
| institution | Kabale University |
| issn | 2198-3844 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-d51e11c1d134401f8b33f200fc0b30342024-12-18T14:18:10ZengWileyAdvanced Science2198-38442024-12-011147n/an/a10.1002/advs.202404437Alphabet Handwriting Recognition: From Wood‐Framed Hydrogel Arrays Design to Machine Learning DecodingGuihua Yan0Xichen Hu1Ziyue Miao2Yongde Liu3Xianhai Zeng4Lu Lin5Olli Ikkala6Bo Peng7College of Environmental Engineering Henan University of Technology Zhengzhou 450001 ChinaDepartment of Applied Physics Aalto University Aalto FI‐00076 FinlandDepartment of Applied Physics Aalto University Aalto FI‐00076 FinlandCollege of Environmental Engineering Henan University of Technology Zhengzhou 450001 ChinaCollege of Energy Xiamen University Xiamen 361102 ChinaCollege of Energy Xiamen University Xiamen 361102 ChinaDepartment of Applied Physics Aalto University Aalto FI‐00076 FinlandDepartment of Applied Physics Aalto University Aalto FI‐00076 FinlandAbstract Handwriting recognition is a highly integrated system, demanding hardware to collect handwriting signals and software to deal with input data. Nonetheless, the design of such a system from scratch with sustainable materials and an easily accessible computing network presents significant challenges. In pursuit of this goal, a flexible, and electrically conductive wood‐derived hydrogel array is developed as a handwriting input panel, enabling recognizing alphabet handwriting assisted by machine learning technique. For this, lignin extraction‐refill, polypyrrole coating, and polyacrylic acid filling, endowing flexibility, and electrical conduction to wood are sequentially implemented. Subsequently, these woods are manufactured into a 5 × 5 array, creating a matrix of signals upon handwriting. Efficient handwritten recognition is then achieved through appropriate manual feature extraction and algorithms with low complexity within a computing network, as demonstrated in this work, the strategic choice of expertise‐based feature engineering and simplified algorithms effectively boost the overall model performance on handwriting recognition. With potential adaptability, further applications in customized wearable devices and hands‐on healthcare appliances are envisioned.https://doi.org/10.1002/advs.202404437handwriting recognitionhydrogelmachine learningwood |
| spellingShingle | Guihua Yan Xichen Hu Ziyue Miao Yongde Liu Xianhai Zeng Lu Lin Olli Ikkala Bo Peng Alphabet Handwriting Recognition: From Wood‐Framed Hydrogel Arrays Design to Machine Learning Decoding Advanced Science handwriting recognition hydrogel machine learning wood |
| title | Alphabet Handwriting Recognition: From Wood‐Framed Hydrogel Arrays Design to Machine Learning Decoding |
| title_full | Alphabet Handwriting Recognition: From Wood‐Framed Hydrogel Arrays Design to Machine Learning Decoding |
| title_fullStr | Alphabet Handwriting Recognition: From Wood‐Framed Hydrogel Arrays Design to Machine Learning Decoding |
| title_full_unstemmed | Alphabet Handwriting Recognition: From Wood‐Framed Hydrogel Arrays Design to Machine Learning Decoding |
| title_short | Alphabet Handwriting Recognition: From Wood‐Framed Hydrogel Arrays Design to Machine Learning Decoding |
| title_sort | alphabet handwriting recognition from wood framed hydrogel arrays design to machine learning decoding |
| topic | handwriting recognition hydrogel machine learning wood |
| url | https://doi.org/10.1002/advs.202404437 |
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