What question did this study set out to answer?

To introduce and evaluate ArCAR, a deep learning computer-aided recognition framework that represents and classifies Arabic text at the character level.

July 16, 2021AlgorithmsOpen Access

ArCAR: A Novel Deep Learning Computer-Aided Recognition for Character-Level Arabic Text Representation and Recognition

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Authors

AMAbdullah Y. MuaadSana'a University HJHanumanthappa Jayappa MAMugahed A. Al–antariCairo University

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Implication

Validation study demonstrates high classification accuracy across Arabic text datasets, highlighting character-level vectorization as a viable method for complex script processing.

Key Points

To introduce and evaluate ArCAR, a deep learning computer-aided recognition framework that represents and classifies Arabic text at the character level.
Quantized input Arabic characters into 1D vectors to generate a 2D array representation for deep learning processing.
Evaluated model performance using 5-fold cross-validation on two tasks: document classification via the Alarabiya-balance dataset and sentiment analysis via the hotel Arabic reviews dataset (HARD) balance dataset.
Achieved 97.76% overall accuracy, 94.08% recall, 94.16% precision, and a 94.09% F1-score on the Alarabiya-balance document classification benchmark.
Achieved 93.58% overall accuracy and a 93.23% F1-score on the HARD balance sentiment analysis dataset.

Cite This Study

Muaad et al. (2021) studied this question.

synapsesocial.com/papers/6a764b71fccf1eb1fb8801fd https://doi.org/10.3390/a14070216

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Implication

Validation study demonstrates high classification accuracy across Arabic text datasets, highlighting character-level vectorization as a viable method for complex script processing.

ArCAR: A Novel Deep Learning Computer-Aided Recognition for Character-Level Arabic Text Representation and Recognition

Discussion

Member takes

Implication

Key Points

Cite This Study

ArCAR: A Novel Deep Learning Computer-Aided Recognition for Character-Level Arabic Text Representation and Recognition

Discussion

Member takes

Implication

Key Points

Cite This Study

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