Generative image captioning in Urdu using deep learning
Issued Date
2023-01-01
Resource Type
ISSN
18685137
eISSN
18685145
Scopus ID
2-s2.0-85152416616
Journal Title
Journal of Ambient Intelligence and Humanized Computing
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Ambient Intelligence and Humanized Computing (2023)
Suggested Citation
Afzal M.K., Shardlow M., Tuarob S., Zaman F., Sarwar R., Ali M., Aljohani N.R., Lytras M.D., Nawaz R., Hassan S.U. Generative image captioning in Urdu using deep learning. Journal of Ambient Intelligence and Humanized Computing (2023). doi:10.1007/s12652-023-04584-y Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/81787
Title
Generative image captioning in Urdu using deep learning
Other Contributor(s)
Abstract
Urdu is morphologically rich language and lacks the resources available in English. While several studies on the image captioning task in English have been published, this is among the pioneer studies on Urdu generative image captioning. The study makes several key contributions: (i) it presents a new dataset for Urdu image captioning, and (ii) it presents different attention-based architectures for image captioning in the Urdu language. These attention mechanisms are new to the Urdu language, as those have never been used for the Urdu image captioning task (iii) Finally, it performs quantitative and qualitative analysis of the results by studying the impact of different model architectures on Urdu’s image caption generation task. The extensive experiments on the Urdu image caption generation task show encouraging results such as a BLEU-1 score of 72.5, BLEU-2 of 56.9, BLEU-3 of 42.8, and BLEU-4 of 31.6. Finally, we present data and code used in the study for future research via GitHub (https://github.com/saeedhas/Urdu_cap_gen).