  Large amount of public data produced by enterprises are in semi-structured
PDF form. Tabular data extraction from reports and other published data in PDF
format is of interest for various data consolidation purposes such as analysing
and aggregating financial reports of a company. Queries into the structured
tabular data in PDF format are normally processed in an unstructured manner
through means like text-match. This is mainly due to that the binary format of
PDF documents is optimized for layout and rendering and do not have great
support for automated parsing of data. Moreover, even the same table type in
PDF files varies in schema, row or column headers, which makes it difficult for
a query plan to cover all relevant tables. This paper proposes a deep learning
based method to enable SQL-like query and analysis of financial tables from
annual reports in PDF format. This is achieved through table type
classification and nearest row search. We demonstrate that using word embedding
trained on Google news for header match clearly outperforms the text-match
based approach in traditional database. We also introduce a practical system
that uses this technology to query and analyse finance tables in PDF documents
from various sources.
