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

Source: [Image Classification for Arabic: Assessing the Accuracy of Direct English to Arabic Translations](<https://www.sota2.com/research/paper/68fa65fe1e209131cea0dd0c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:41:29.853Z

Authors: Abdulkareem Alsudais

Published: 2018-07-13T17:44:20.000Z

## 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 Arabic labels were accurate. This study makes three important contributions to the image classification literature: (1) it determined the baseline level of accuracy for algorithms that provide Arabic labels for images, (2) it provided 1,895 images that are tagged with accurate Arabic labels, and (3) provided the accuracy of translations of image labels from English to Arabic.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1807.05206>)

[PDF](<https://arxiv.org/pdf/1807.05206.pdf>)