Research on AI Empowered Pen Writing Training

In response to the problems of low efficiency, weak personalization, and lack of high-quality teachers in pen writing training for normal students, this paper proposes a multi-round training mode of pen writing for normal students based on the Xiaoya intelligent teaching platform. Relying on the AI-based quantitative evaluation of standardized Chinese character writing, this model provides a holistic and detailed evaluation from three dimensions: alignment, stretch and uniformity. In the detailed evaluation, it not only provides scores for handwriting samples but also feeds back detailed descriptions of the problems for each writing point, so that students can more accurately understand the handwriting problems in order to correct the writing errors more pertinently.

Authors

Institutions

Publication Details

Published
2024-09-13
DOI
https://doi.org/10.1109/icet62460.2024.10868480
Primary Topic
Handwritten Text Recognition Techniques
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on AI Empowered Pen Writing Training

Chuang Zhu, Wan Ma, Jiangbo Shu, Shanfei Shi
Handwritten Text Recognition Techniques
article

Research on AI Empowered Pen Writing Training

Chuang Zhu, Wan Ma, Jiangbo Shu, Shanfei Shi
article en

Abstract

In response to the problems of low efficiency, weak personalization, and lack of high-quality teachers in pen writing training for normal students, this paper proposes a multi-round training mode of pen writing for normal students based on the Xiaoya intelligent teaching platform. Relying on the AI-based quantitative evaluation of standardized Chinese character writing, this model provides a holistic and detailed evaluation from three dimensions: alignment, stretch and uniformity. In the detailed evaluation, it not only provides scores for handwriting samples but also feeds back detailed descriptions of the problems for each writing point, so that students can more accurately understand the handwriting problems in order to correct the writing errors more pertinently.

Central China Normal University (CN)
Ministry of Education, National Science and Technology Major Project
Quality Education
Openalex Percentile: Top 73%
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.