OCRFlux-pubtabnet-cross

PDF documents are typically paginated, which often results in tables or paragraphs being split across consecutive pages. Accurately detecting and merging such cross-page structures is crucial to avoid generating incomplete or fragmented content.

The merging of two table fragments is especially challenging. For example, the table spanning multiple pages will repeat the header of the first page on the second page. Another difficult scenario is that the table cell contains long content that spans multiple lines within the cell, with the first few lines appearing on the previous page and the remaining lines continuing on the next page. We also observe some cases where tables with a large number of columns are split vertically and placed on two consecutive pages.

OCRFlux-pubtabnet-cross is a benchmark of 9064 samples which can be used to measure the performance of OCR systems in cross-page table merging.

Quick links:

• 🤗 Model
• 🛠️ Code

Data Mix

We generate the dataset by splitting each original table in OCRFlux-pubtabnet-single through diverse splitting strategies simulating the real-world scenarios of cross-page table segmentation.

Data Format

Each row in the dataset corresponds to two table fragments and their corresponding ground-truth merged versions, all in HTML format.

Features:

{
    'image_name': string,         # Name of the original table image
    'type': string,         # Type of the original table, "simple" or "complex"
    'gt_table': string,           # Ground-truth HTML of the original table
    'table_fragment_1': string,             # HTML of the first table fragment
    'table_fragment_2': string,             # HTML of the second table fragment
}

License

This dataset is licensed under Apache-2.0.