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

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 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.