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Analyzing Arabic Handwriting Style through Hand Kinematics
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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.
Title: Analyzing Arabic Handwriting Style through Hand Kinematics
Description:
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.
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