Efficient Urdu Caption Generation using Attention based LSTM
Abstract
Recent advancements in deep learning have created many opportunities to solve real-world problems that remained unsolved for more than a decade. Automatic caption generation is a major research field, and the research community has done a lot of work on it in most common languages like English. Urdu is the national language of Pakistan and also much spoken and understood in the sub-continent region of Pakistan-India, and yet no work has been done for Urdu language caption generation. Our research aims to fill this gap by developing an attention-based deep learning model using techniques of sequence modeling specialized for the Urdu language. We have prepared a dataset in the Urdu language by translating a subset of the "Flickr8k" dataset containing 700 'man' images. We evaluate our proposed technique on this dataset and show that it can achieve a BLEU score of 0.83 in the Urdu language. We improve on the previous state-of-the-art by using better CNN architectures and optimization techniques. Furthermore, we provide a discussion on how the generated captions can be made correct grammar-wise.
Keywords
Cite
@article{arxiv.2008.01663,
title = {Efficient Urdu Caption Generation using Attention based LSTM},
author = {Inaam Ilahi and Hafiz Muhammad Abdullah Zia and Muhammad Ahtazaz Ahsan and Rauf Tabassam and Armaghan Ahmed},
journal= {arXiv preprint arXiv:2008.01663},
year = {2021}
}
Comments
This a project report of Deep Learning subject taught at Information Technology University, Lahore, Pakistan by Dr. Mohsen Ali