ICDAR 2023 CROHME: Competition on Recognition of Handwritten Mathematical Expressions

Here is the datasets collected for the Competitionon Recognition of Online Handwritten Mathematical Expressions in competition session of ICDAR 2023.  
3 tasks are proposed with different modalities, there are on-line, off-line and bi-modal.  
For on-line task, we provide .inkml file (contain trace information, mathML and LaTeX string), and also symbol level label graph (SymLG) as ground truth. Except the new data and previous CROHME data, we also provide huge amount of artificial on-line data in the train set.   
For off-line task, the .png images (scanned from paper or rendering from inkml) and symbol level label graph (SymLG) are provided. Except the new data and previous CROHME data, we use off-line images from OffHME to increase the size of train set.  
For bi-modal task, both .inkml file and ,png images are provided as 2 channels input, and SymLG as ground truth.  

All the 3 tasks inherited the data collected from the previous 6 CROHME, and also the new collection 2023 in 3 sites, Nantes (France), Luleå (Sweden) and Tokyo (Japan).

本数据集为ICDAR 2023竞赛环节中的在线手写数学表达式识别竞赛所收集的数据集。
本次竞赛设置了三种不同模态的任务，分别为在线任务、离线任务与双模态任务。
针对在线任务，我们提供.inkml文件（包含轨迹信息、数学标记语言（MathML）与LaTeX字符串），以及符号级标签图（SymLG, Symbol Level Label Graph）作为真值标签。除新增数据与往届CROHME竞赛数据集外，训练集还包含海量人工生成的在线手写数据。
针对离线任务，我们提供.png图像（由纸质文档扫描所得或由.inkml文件渲染生成）与符号级标签图（SymLG）作为真值标签。除新增数据与往届CROHME竞赛数据集外，我们还引入OffHME的离线图像以扩充训练集规模。
针对双模态任务，我们同时提供.inkml文件与.png图像作为双通道输入，并以符号级标签图（SymLG）作为真值标签。
三项任务均沿用了此前六届CROHME竞赛所收集的数据集，同时新增了来自法国南特、瑞典吕勒奥与日本东京三个站点的2023年新征集数据。