Image Classification for Arabic: Assessing the Accuracy of Direct English to Arabic Translations
Abdulkareem Alsudais

TL;DR
This study evaluates the accuracy of direct English to Arabic translations of image labels from ImageNet, revealing a baseline accuracy of 65.6% for Arabic labels in image classification tasks.
Contribution
It introduces a method for generating Arabic labels via direct translation and assesses its accuracy, providing valuable data for Arabic image classification research.
Findings
65.6% of Arabic labels were accurate
Provided 1,895 images with accurate Arabic labels
Established baseline accuracy for Arabic label translation
Abstract
Image classification is an ongoing research challenge. Most of the available research focuses on image classification for the English language, however there is very little research on image classification for the Arabic language. Expanding image classification to Arabic has several applications. The present study investigated a method for generating Arabic labels for images of objects. The method used in this study involved a direct English to Arabic translation of the labels that are currently available on ImageNet, a database commonly used in image classification research. The purpose of this study was to test the accuracy of this method. In this study, 2,887 labeled images were randomly selected from ImageNet. All of the labels were translated from English to Arabic using Google Translate. The accuracy of the translations was evaluated. Results indicated that that 65.6% of the…
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