ICDAR 2021 Scientific Literature Parsing Competition

ICDAR 2021 Scientific Literature Parsing Competition

Antonio Jimeno Yepes, Peter Zhong, Douglas Burdick

04 September 2021

Documents in Portable Document Format (PDF) are ubiquitous with over 2.5 trillion documents. PDF format is human readable but not easily understood by machines and the large number of different styles makes it difficult to process the large variety of documents effectively. Our ICDAR 2021 Scientific Literature Parsing Competition offers participants with a large number of training and evaluation examples compared to previous competitions. Top competition results show a significant increase in performance compared to previously reported on the competition data sets. Most of the current methods for document understanding rely on deep learning, which requires a large number of training examples. We have generated large data sets that have been used in this competition. Our competition is split into two tasks to understand document layouts (Task A) and tables (Task B). 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.


Venue : 16th International Conference on Document Analysis and Recognition