A stroke-level large-scale database of Chinese character handwriting and the OpenHandWrite_Toolbox for handwriting research

Understanding what linguistic components (e.g., phonological, semantic, and orthographic systems) modulate Chinese handwriting at the character, radical, and stroke levels remains an important yet understudied topic. Additionally, there is a lack of comprehensive tools for capturing and batch-processing fine-grained handwriting data. To address these issues, we constructed a large-scale handwriting database in which 42 Chinese speakers each handwrote 1200 characters in a handwriting-to-dictation task. Additionally, we enhanced the existing handwriting package and provided comprehensive documentation for the upgraded OpenHandWrite_Toolbox. This toolbox allows users to easily modify the experimental design, capture the stroke-level handwriting trajectory, and batch-process handwriting measurements (e.g., latency and duration). Analysis of our large-scale database indicates that orthographic predictors affect handwriting preparation and execution at the character, radical, and stroke levels. Phonological factors influence handwriting at character and radical levels. Importantly, these lexical effects demonstrate hierarchical attenuation: they were most pronounced at the character level, followed by the radical, and weakest at the stroke level. These findings demonstrate that handwriting preparation and execution at the radical and stroke levels are closely intertwined with linguistic components. This database and toolbox offer valuable resources for future psycholinguistic and neurolinguistic research on the handwriting of characters and sub-characters across different languages.

Authors

Institutions

Publication Details

Journal
Behavior Research Methods
Published
2026-05-11
DOI
https://doi.org/10.3758/s13428-026-03001-4
Primary Topic
Handwritten Text Recognition Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A stroke-level large-scale database of Chinese character handwriting and the OpenHandWrite_Toolbox for handwriting research

Guido Nottbusch, Zebo Xu, Shaoyun Yu, Zhenguang Garry Cai et al.
Behavior Research Methods
Handwritten Text Recognition Techniques
article

A stroke-level large-scale database of Chinese character handwriting and the OpenHandWrite_Toolbox for handwriting research

Guido Nottbusch, Zebo Xu, Shaoyun Yu, Zhenguang Garry Cai, Mark Torrance, Nan Zhao
article en

Abstract

Understanding what linguistic components (e.g., phonological, semantic, and orthographic systems) modulate Chinese handwriting at the character, radical, and stroke levels remains an important yet understudied topic. Additionally, there is a lack of comprehensive tools for capturing and batch-processing fine-grained handwriting data. To address these issues, we constructed a large-scale handwriting database in which 42 Chinese speakers each handwrote 1200 characters in a handwriting-to-dictation task. Additionally, we enhanced the existing handwriting package and provided comprehensive documentation for the upgraded OpenHandWrite_Toolbox. This toolbox allows users to easily modify the experimental design, capture the stroke-level handwriting trajectory, and batch-process handwriting measurements (e.g., latency and duration). Analysis of our large-scale database indicates that orthographic predictors affect handwriting preparation and execution at the character, radical, and stroke levels. Phonological factors influence handwriting at character and radical levels. Importantly, these lexical effects demonstrate hierarchical attenuation: they were most pronounced at the character level, followed by the radical, and weakest at the stroke level. These findings demonstrate that handwriting preparation and execution at the radical and stroke levels are closely intertwined with linguistic components. This database and toolbox offer valuable resources for future psycholinguistic and neurolinguistic research on the handwriting of characters and sub-characters across different languages.

Behavior Research MethodsVol. 58(6)
Hong Kong Baptist University (HK), University of Potsdam (DE), Chinese University of Hong Kong (HK), University of Applied Sciences Potsdam (DE), Nottingham Trent University (GB)
Openalex Percentile: Top 99%
Handwritten Text Recognition Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.