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Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis

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Analyzing Arabic Handwriting Style through Hand Kinematics.

Vahan Babushkin^1,2, Haneen Alsuradi¹, Muhamed Osman Al-Khalil³

¹Applied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi P.O. Box 129188, United Arab Emirates.

Sensors (Basel, Switzerland)

|October 16, 2024

Summary

This summary is machine-generated.

This study introduces a novel method for classifying Arabic handwriting styles using dynamic kinematic data, achieving 88% accuracy. The research identifies key features like hand speed and pressure crucial for accurate style detection.

Keywords:

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

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Area of Science:

Computer Science
Human-Computer Interaction
Linguistics

Background:

Arabic handwriting features multiple styles (e.g., Ruq'ah, Naskh), complicating consistent style adherence.
Existing research primarily analyzes static documents, overlooking dynamic features and mixed styles.

Purpose of the Study:

To develop and evaluate a model for classifying Arabic handwriting based on style adherence using dynamic features.
To identify critical kinematic features influencing Arabic handwriting style classification.

Main Methods:

Collected dynamic stylus and hand kinematic data from 50 participants writing Arabic text.
Developed a classification model trained on pure, mixed, and non-styled handwriting samples.
Utilized parameter search for optimal model hyperparameters, sliding window length, and overlap.

Main Results:

The proposed model achieved 88% accuracy in classifying Arabic handwriting samples into four style adherence classes.
Explainability analysis (Shapley values) highlighted hand speed, pressure, and pen slant as significant features.
Other kinematic features contributed nearly equally to the classification performance.

Conclusions:

Dynamic kinematic features offer a robust approach to classifying Arabic handwriting style adherence.
Hand speed, pressure, and pen slant are key indicators for distinguishing Arabic handwriting styles.
The study provides insights into the dynamic characteristics that define Arabic handwriting styles.