Scientific literature contain important information related to cutting-edge
innovations in diverse domains. Advances in natural language processing have
been driving the fast development in automated information extraction from
scientific literature. However, scientific literature is often available in
unstructured PDF format. While PDF is great for preserving basic visual
elements, such as characters, lines, shapes, etc., on a canvas for presentation
to humans, automatic processing of the PDF format by machines presents many
challenges. With over 2.5 trillion PDF documents in existence, these issues are
prevalent in many other important application domains as well.
Our ICDAR 2021 Scientific Literature Parsing Competition (ICDAR2021-SLP) aims
to drive the advances specifically in document understanding. ICDAR2021-SLP
leverages the PubLayNet and PubTabNet datasets, which provide hundreds of
thousands of training and evaluation examples. In Task A, Document Layout
Recognition, submissions with the highest performance combine object detection
and specialised solutions for the different categories. In Task B, Table
Recognition, top submissions rely on methods to identify table components and
post-processing methods to generate the table structure and content. Results
from both tasks show an impressive performance and opens the possibility for
high performance practical applications.

本文介绍了科技文献对自然语言处理的挑战和 ICDAR 2021 科技文献解析竞赛的使用，并展示了该竞赛在文档布局识别和表格识别方面的高性能成果。