  While India has been one of the hotspots of COVID-19, data about the pandemic
from the country has proved to be largely inaccessible at scale. Much of the
data exists in unstructured form on the web, and limited aspects of such data
are available through public APIs maintained manually through volunteer effort.
This has proved to be difficult both in terms of ease of access to detailed
data and with regards to the maintenance of manual data-keeping over time. This
paper reports on our effort at automating the extraction of such data from
public health bulletins with the help of a combination of classical PDF parsers
and state-of-the-art machine learning techniques. In this paper, we will
describe the automated data-extraction technique, the nature of the generated
data, and exciting avenues of ongoing work.
