IBEM: A Dataset for Indexing and Search of Mathematical Expressions in Scientific Document Images

IBEM (Indexing and Search of Mathematical Expressions) is a dataset designed for research on mathematical expression indexing, search, and recognition in scientific document images. It contains more than 8000 page images extracted from 600 scientific documents together with ground-truth annotations for both displayed (29596) and inline (137095) mathematical expressions. Each annotated expression includes its bounding box coordinates, the original LaTeX transcription, several normalized LaTeX representations, and a query-oriented canonical representation suitable for indexing and search. Document-specific LaTeX preambles are also provided to facilitate faithful rendering and interpretation of the annotated expressions. Besides the page images and annotation files, the dataset includes predefined evaluation partitions as provided for the ICDAR 2021 Competition on Mathematical Formula Detection, and two utility scripts. The script (extract_GT.py) extracts image/LaTeX ground-truth pairs suitable for mathematical expression recognition benchmarks. The second script (extract_subexp.py) performs structure-aware segmentation of mathematical expressions, generating hierarchical sub-expressions from delimiter scopes and symbolic operators. These segments can be used for probabilistic indexing, partial-expression spotting, and other search-oriented applications.