A Transfer Learning Approach for Arabic Image Captions

Abstract

Background: Arabic image captioning (AIC) is the automatic generation of text descriptions in the Arabic language for images. Applies a transfer learning approach in deep learning to enhance computer vision and natural language processing. There are many datasets in English reverse other languages. Instead of, the Arabs researchers unanimously agreed that there is a lack of Arabic databases available in this field. Objective: This paper presents the improvement and processing of the available Arabic textual database using Google spreadsheets for translation and creation of AR. Flicker8k2023 dataset is an extension of the Arabic Flicker8k dataset available, it was uploaded to GitHub and made public for researches. Methods: An efficient model proposed using deep learning techniques by including two pre-training models (VGG16 and VGG19), to extract features from the images and build (LSTM and GRU) models to process textual prediction sequence. In addition to the effect of pre-processing the text in Arabic. Results: The adopted model outperforms better compared to the previous study in BLEU-1 from 33 to 40. Conclusions: This paper concluded that the biggest problem is the database available in the Arabic language. This paper has worked to increase the size of the text database from 24,276 to 32,364 thousand captions, where each image contains 4 captions.