Recent advancements in deep learning has created a lot of opportunities to solve those real world problems which remained unsolved for more than a decade. Automatic caption generation is a major research field, and  research community has done a lot of work on this problem on 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 modelling specialized for Urdu language. We have prepared a dataset in  Urdu language by translating a subset of  "Flickr8k" dataset containing 700 'man' images. We evaluate our proposed technique on this dataset and show that it is able to achieve a BLEU score of 0.83 on Urdu language. We improve on the previously proposed techniques by using better CNN architectures and optimization techniques. Furthermore, we also tried adding a grammar loss to the model in order to make the predictions grammatically correct .
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 .
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