Searching in massive collections of digitized printed scientific documents with queries that are mathematical expressions is a research area scarcely explored. To address this problem, a crucial first step involves the detection of regions that may contain mathematical expressions. This contest aims to tackle this problem and thus, provide several reasons that could be interesting for attracting research groups to participate in this competition:
- Groups researching in Mathematical Expression Recognition, at some point, need to address the problem of automatic detection of mathematical expressions in a document;
- Participants in this contest will have access to a large labeled dataset;
- The method of obtaining labeled data in the IBEM corpus is scalable, so it is expected to increase this collection in the future, and this new data could be used in future editions of this contest.
Method Description
We built our approach on FCOS, A simple and strong anchor-free object detector, with ResNeSt as our backbone, to detect embedded and isolated formulas. We employed ATSS as our sampling strategy instead of random sampling to eliminate the effects of sample imbalance. Moreover, we observed and revealed the influence of different FPN levels on the detection result. Generalized Focal Loss is adopted to our loss. Finally, with a series of useful tricks and model ensembles, our method was ranked 1st in the MFD task.
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Prerequisites
- Linux or macOS (Windows is in experimental support)
- Python 3.6
- PyTorch 1.3
- CUDA 9.2 (If you build PyTorch from source, CUDA 9.0 is also compatible)
- GCC 5
- MMCV
This project is based on MMDetection-v2.7.0, mmcv-full>=1.1.5, <1.3 is needed.Note: You need to run pip uninstall mmcv first if you have mmcv installed.If mmcv and mmcv-full are both installed, there will be ModuleNotFoundError.
