Efficient Urdu Caption Generation using Attention based LSTMs
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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.
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