Functional brain networks underlying the interaction between central and peripheral processes involved in Chinese handwriting in children and adults

Li, Junjun; Liu, Ying; Wang, Yi; Wang, Nizhuan; Ji, Yuzhu; Wei, Tongqi; Bi, Hong‐Yan; Yang, Yang

doi:10.1002/hbm.26055

Cited by 5 publications

(1 citation statement)

References 75 publications

(121 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One possible reason for these associations could be due to good penmanship requires lots of handwriting practices, which is highly interactive with the linguistic processes (e.g., orthographic lexicon, semantic system, phonological lexicon and so on). For instance, previous studies on Chinese handwriting have been shown that lexical variables, such as character frequency and regularity etc, modulating the central and peripheral processes of handwriting (Wang et al, 2020;Huang, Zhou, et al, 2021;Huang, Lin, et al, 2021;neuroimaging studies: Yang et al, 2022;Li et al, 2023). Therefore, the end product of handwriting: penmanship, may inherit crucial details of individuals' language competence, However, there are currently not many automatic tools for penmanship assessment.…”

Section: Assessing Handwritingmentioning

confidence: 99%

Assessing Penmanship of Chinese Handwriting: A Deep Learning-based Approach

Xu,

Mittal,

Ahmed

et al. 2024

Preprint

View full text Add to dashboard Cite

The rise of the digital era has led to a decline in handwriting as the primary mode of communication, resulting in negative effects on handwriting literacy, particularly in complex writing systems such as Chinese. The marginalization of handwriting has contributed to the deterioration of penmanship, defined as the ability to write aesthetically and legibly. Despite penmanship being widely acknowledged as a crucial factor in predicting language literacy, research on its evaluation remains limited and in the early stages, with existing assessments primarily dependent on expert subjective ratings. Recent initiatives have started to explore the application of convolutional neural networks (CNN) for automated penmanship assessment. In this study, we adopt a similar approach, developing a CNN-based automatic assessment system for penmanship in traditional Chinese handwriting. Utilizing an existing database of 39,207 accurately handwritten characters (penscripts) from 40 handwriters, we had three human raters evaluate each penscript’s penmanship on a 10-point scale and calculate an average penmanship score. We trained a CNN on 90% of the penscripts and their corresponding penmanship scores. Upon testing the CNN model on the remaining 10% of penscripts, it achieved a remarkable performance (overall 9.82% normalized Mean Absolute Percentage Error) in predicting human penmanship scores, illustrating its potential for assessing handwriters' penmanship. To enhance accessibility, we developed a mobile application based on the CNN model, allowing users to conveniently evaluate their penmanship.

show abstract

Section: Assessing Handwritingmentioning

confidence: 99%

Assessing Penmanship of Chinese Handwriting: A Deep Learning-based Approach

Xu,

Mittal,

Ahmed

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Functional brain networks underlying the interaction between central and peripheral processes involved in Chinese handwriting in children and adults

Liu

Wang

et al. 2022

Human Brain Mapping

View full text Add to dashboard Cite

The neural mechanisms that support handwriting, an important mode of human communication, are thought to be controlled by a central process (responsible for spelling) and a peripheral process (responsible for motor output). However, the relationship between central and peripheral processes has been debated. Using functional magnetic resonance imaging, this study examined the neural mechanisms underlying this relationship in Chinese handwriting in 36 children (mean age = 10.40 years) and 56 adults (mean age = 22.36 years) by manipulating character frequency (a central variable). Brain network analysis showed that character frequency reconfigured functional brain networks known to underlie motor processes, including the somatomotor and cerebellar network, in both children and adults, indicating that central processing cascades into peripheral processing. Furthermore, the network analysis characterized the interaction profiles between motor networks and linguistic‐cognitive networks, fully mapping the neural architecture that supports the interaction of central and peripheral processes involved in handwriting. Taken together, these results reveal the neural interface underlying the interaction between central and peripheral processes involved in handwriting in a logographic writing system, advancing our understanding of the neural basis of handwriting.

show abstract

Assessing penmanship of Chinese handwriting: a deep learning-based approach

Xu,

Mittal,

Ahmed

et al. 2024

Read Writ

View full text Add to dashboard Cite

The rise of the digital era has led to a decline in handwriting as the primary mode of communication, resulting in negative effects on handwriting literacy, particularly in complex writing systems such as Chinese. The marginalization of handwriting has contributed to the deterioration of penmanship, defined as the ability to write aesthetically and legibly. Despite penmanship being widely acknowledged as a crucial factor in predicting language literacy, research on its evaluation remains limited, with existing assessments primarily dependent on expert subjective ratings. Recent initiatives have started to explore the application of convolutional neural networks (CNN) for automated penmanship assessment. In this study, we adopted a similar approach, developing a CNN-based automatic assessment system for penmanship in traditional Chinese handwriting. Utilizing an existing database of 39,207 accurately handwritten characters (penscripts) from 40 handwriters, we had three human raters evaluate each penscript’s penmanship on a 10-point scale and calculated an average penmanship score. We trained a CNN on 90% of the penscripts and their corresponding penmanship scores. Upon testing the CNN model on the remaining 10% of penscripts, it achieved a remarkable performance (overall 9.82% normalized Mean Absolute Percentage Error) in predicting human penmanship scores, illustrating its potential for assessing handwriters’ penmanship. To enhance accessibility, we developed a mobile application based on the CNN model, allowing users to conveniently evaluate their penmanship.

show abstract

Functional brain networks underlying the interaction between central and peripheral processes involved in Chinese handwriting in children and adults

Cited by 5 publications

References 75 publications

Assessing Penmanship of Chinese Handwriting: A Deep Learning-based Approach

Assessing Penmanship of Chinese Handwriting: A Deep Learning-based Approach

Functional brain networks underlying the interaction between central and peripheral processes involved in Chinese handwriting in children and adults

Assessing penmanship of Chinese handwriting: a deep learning-based approach

Contact Info

Product

Resources

About