Handwriting Trajectory Recovery of Latin Characters with Deep Learning: A Novel Exploring the Amount of Points per Character and New Evaluation Method

Simone Bello Kaminski Aires, Erikson Freitas de Morais, Yu Han Lin

2025

Abstract

The research on handwriting trajectory recovery (HTR) has gained prominence in offline handwriting recognition by utilizing online recognition resources to simulate writing patterns. Traditional approaches commonly employ graph-based methods that skeletonize characters to trace their paths, while recent studies have focused on deep learning techniques due to their superior generalization capabilities. However, despite promising results, the absence of standardized evaluation metrics limits meaningful comparisons across studies. This work presents a novel approach to recovering handwriting trajectories of Latin characters using deep learning networks, coupled with a standardized evaluation framework. The proposed evaluation model quantitatively and qualitatively assesses the recovery of stroke sequences and character geometry, providing a consistent basis for comparison. Experimental results demonstrate the significant influence of the number of coordinate points per character on deep learning performance, offering valuable insights into optimizing both evaluation and recovery rates. This study provides a practical solution for enhancing HTR accuracy and establishing a standardized evaluation methodology.

Paper Citation

in Harvard Style

Aires S., Freitas de Morais E. and Lin Y. (2025). Handwriting Trajectory Recovery of Latin Characters with Deep Learning: A Novel Exploring the Amount of Points per Character and New Evaluation Method. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 855-862. DOI: 10.5220/0013383100003912

in Bibtex Style

@conference{visapp25,
author={Simone Aires and Erikson Freitas de Morais and Yu Lin},
title={Handwriting Trajectory Recovery of Latin Characters with Deep Learning: A Novel Exploring the Amount of Points per Character and New Evaluation Method},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={855-862},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013383100003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Handwriting Trajectory Recovery of Latin Characters with Deep Learning: A Novel Exploring the Amount of Points per Character and New Evaluation Method
SN - 978-989-758-728-3
AU - Aires S.
AU - Freitas de Morais E.
AU - Lin Y.
PY - 2025
SP - 855
EP - 862
DO - 10.5220/0013383100003912
PB - SciTePress