ICDAR2019 Competition on Recognition of Documents with Complex Layouts  RDCL2019 This paper presents an objective comparative evaluation of page segmentation and region classification methods for documents with complex layouts. It describes the competition (modus operandi, dataset and evaluation methodology) held in the context of ICDAR2019, presenting the results of the evaluation of twelve methods  nine submitted, three stateoftheart systems (commercial and opensource). Three scenarios are reported in this paper, one evaluating the ability of methods to accurately segment regions and two evaluating both segmentation and region classification. Text recognition was a bonus challenge and was not taken up by all participants. The results indicate that an innovative approach has a clear advantage but there is still a considerable need to develop robust methods that deal with layout challenges, especially with the nontextual content.

ICDAR 2019 Competition on Table Detection and Recognition (cTDaR) The cTDaR competition aims at benchmarking stateoftheart table detection (TRACK A) and table recognition (TRACK B) methods. In particular, we wish to investigate and compare general methods that can reliably and robustly identify the table regions within a document image on the one hand, and the table structure on the other hand. Due to the presence of handdrawn tables and handwritten text, the methods must be robust against various noise conditions, interfering annotations, and variations of the tables. Two new challenging datasets were created to test the behaviour of stateoftheart table detection and recognition systems on real world data. One dataset consists of modern documents, while the other consists of archival documents with presence of handdrawn tables and handwritten text. The evaluation scheme is adapted from the ICDAR 2013 Table competition. We received results of Track A from 11 teams and results of Track B from 2 teams. Results for Track A are very good for the top participants. The winner and his runnerup are very close while using very different approaches. Track B was more challenging and only one participant was able to produce good results.