  Tables present important information concisely in many scientific documents.
Visual features like mathematical symbols, equations, and spanning cells make
structure and content extraction from tables embedded in research documents
difficult. This paper discusses the dataset, tasks, participants' methods, and
results of the ICDAR 2021 Competition on Scientific Table Image Recognition to
LaTeX. Specifically, the task of the competition is to convert a tabular image
to its corresponding LaTeX source code. We proposed two subtasks. In Subtask 1,
we ask the participants to reconstruct the LaTeX structure code from an image.
In Subtask 2, we ask the participants to reconstruct the LaTeX content code
from an image. This report describes the datasets and ground truth
specification, details the performance evaluation metrics used, presents the
final results, and summarizes the participating methods. Submission by team
VCGroup got the highest Exact Match accuracy score of 74% for Subtask 1 and 55%
for Subtask 2, beating previous baselines by 5% and 12%, respectively. Although
improvements can still be made to the recognition capabilities of models, this
competition contributes to the development of fully automated table recognition
systems by challenging practitioners to solve problems under specific
constraints and sharing their approaches; the platform will remain available
for post-challenge submissions at
https://competitions.codalab.org/competitions/26979 .
