Evidence map›Paper›PMID 39409395›Full record

ArticleSensors (Basel, Switzerland)2024

Analyzing Arabic Handwriting Style through Hand Kinematics.

Vahan Babushkin, Haneen Alsuradi, Muhamed Osman Al-Khalil, Mohamad Eid

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Analyzing handwriting legibility through hand kinematics.Frontiers in artificial intelligence · 2025
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Vahan BabushkinApplied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi P.O. Box 129188, United Arab Emirates.ORCID 0000-0002-3730-9496
Haneen AlsuradiApplied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi P.O. Box 129188, United Arab Emirates.ORCID 0000-0001-7396-444X
Muhamed Osman Al-KhalilArabic Studies Program, New York University Abu Dhabi, Abu Dhabi P.O. Box 129188, United Arab Emirates.ORCID 0009-0006-5800-0558
Mohamad EidApplied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi P.O. Box 129188, United Arab Emirates.ORCID 0000-0002-6940-7891

Funding

ASPIRE Award for Research Excellence (AARE- 2019) program AARE19-159NYUAD Center for Artificial Intelligence and Robotics (CAIR), funded by Tamkeen under the NYUAD Research Institute Award CG010
6 · The paper itself

Abstract

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.

Indexed as

deep learninghandwritinghandwriting stylemachine learningsensorimotor learningtemporal convolutional networks

Identifiers

PMID39409395
PMCPMC11478569

What OpenQuestion holds

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.