Analyzing Arabic Handwriting Style through Hand Kinematics.

deep learning handwriting handwriting style machine learning sensorimotor learning temporal convolutional networks

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
30 Sep 2024
Historique:
received: 28 06 2024
revised: 17 09 2024
accepted: 24 09 2024
medline: 16 10 2024
pubmed: 16 10 2024
entrez: 16 10 2024
Statut: epublish

Résumé

Handwriting style is an important aspect affecting the quality of handwriting. Adhering to one style is crucial for languages that follow cursive orthography and possess multiple handwriting styles, such as Arabic. The majority of available studies analyze Arabic handwriting style from static documents, focusing only on pure styles. In this study, we analyze handwriting samples with mixed styles, pure styles (Ruq'ah and Naskh), and samples without a specific style from dynamic features of the stylus and hand kinematics. We propose a model for classifying handwritten samples into four classes based on adherence to style. The stylus and hand kinematics data were collected from 50 participants who were writing an Arabic text containing all 28 letters and covering most Arabic orthography. The parameter search was conducted to find the best hyperparameters for the model, the optimal sliding window length, and the overlap. The proposed model for style classification achieves an accuracy of 88%. The explainability analysis with Shapley values revealed that hand speed, pressure, and pen slant are among the top 12 important features, with other features contributing nearly equally to style classification. Finally, we explore which features are important for Arabic handwriting style detection.

Identifiants

pubmed: 39409395
pii: s24196357
doi: 10.3390/s24196357
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : ASPIRE Award for Research Excellence (AARE- 2019) program
ID : AARE19-159
Organisme : NYUAD Center for Artificial Intelligence and Robotics (CAIR), funded by Tamkeen under the NYUAD Research Institute Award
ID : CG010

Auteurs

Vahan Babushkin (V)

Applied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi P.O. Box 129188, United Arab Emirates.
Tandon School of Engineering, New York University, New York, NY 11201, USA.

Haneen Alsuradi (H)

Applied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi P.O. Box 129188, United Arab Emirates.

Muhamed Osman Al-Khalil (MO)

Arabic Studies Program, New York University Abu Dhabi, Abu Dhabi P.O. Box 129188, United Arab Emirates.

Mohamad Eid (M)

Applied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi P.O. Box 129188, United Arab Emirates.

Classifications MeSH