# Efficient Urdu Caption Generation using Attention based LSTM

arXiv: https://arxiv.org/abs/2008.01663 · HTML: https://mlsanity.com/paper/2008.01663/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [Constructing Multilingual Visual-Text Datasets Revealing Visual Multilingual Ability of Vision Language Models](https://mlsanity.com/paper/2406.15359/) (2024)
- [Image captioning in different languages](https://mlsanity.com/paper/2407.09495/) (2024)
- [COCO-Urdu: A Large-Scale Urdu Image-Caption Dataset with Multimodal Quality Estimation](https://mlsanity.com/paper/2509.09014/) (2025)

## Abstract (arXiv)

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.