  The quarterly financial statement, or Form 10-Q, is one of the most
frequently required filings for US public companies to disclose financial and
other important business information. Due to the massive volume of 10-Q filings
and the enormous variations in the reporting format, it has been a
long-standing challenge to retrieve item-specific information from 10-Q filings
that lack machine-readable hierarchy. This paper presents a solution for
itemizing 10-Q files by complementing a rule-based algorithm with a
Convolutional Neural Network (CNN) image classifier. This solution demonstrates
a pipeline that can be generalized to a rapid data retrieval solution among a
large volume of textual data using only typographic items. The extracted
textual data can be used as unlabeled content-specific data to train
transformer models (e.g., BERT) or fit into various field-focus natural
language processing (NLP) applications.
