  Many populous countries including India are burdened with a considerable
backlog of legal cases. Development of automated systems that could process
legal documents and augment legal practitioners can mitigate this. However,
there is a dearth of high-quality corpora that is needed to develop such
data-driven systems. The problem gets even more pronounced in the case of low
resource languages such as Hindi. In this resource paper, we introduce the
Hindi Legal Documents Corpus (HLDC), a corpus of more than 900K legal documents
in Hindi. Documents are cleaned and structured to enable the development of
downstream applications. Further, as a use-case for the corpus, we introduce
the task of bail prediction. We experiment with a battery of models and propose
a Multi-Task Learning (MTL) based model for the same. MTL models use
summarization as an auxiliary task along with bail prediction as the main task.
Experiments with different models are indicative of the need for further
research in this area. We release the corpus and model implementation code with
this paper: https://github.com/Exploration-Lab/HLDC
