ICDAR 2019 Competition on Harvesting Raw Tables from Infographics (CHARTInfographics) This work summarizes the results of the first Competition on Harvesting Raw Tables from Infographics (ICDAR 2019 CHARTInfographics). The complex process of automatic chart recognition is divided into multiple tasks for the purpose of this competition, including Chart Image Classification (Task 1), Text Detection and Recognition (Task 2), Text Role Classification (Task 3), Axis Analysis (Task 4), Legend Analysis (Task 5), Plot Element Detection and Classification (Task 6.a), Data Extraction (Task 6.b), and EndtoEnd Data Extraction (Task 7). We provided a large synthetic training set and evaluated submitted systems using newly proposed metrics on both synthetic charts and manuallyannotated real charts taken from scientific literature. A total of 8 groups registered for the competition out of which 5 submitted results for tasks 15. The results show that some tasks can be performed highly accurately on synthetic data, but all systems did not perform as well on real world charts. The data, annotation tools, and evaluation scripts have been publicly released for academic use.

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.