---
license: other
pretty_name: "Fintabnet-Logical"
tags:
- table-structure-recognition
- table-understanding
- document-ai
- computer-vision
- finance
---

# Fintabnet-Logical

## Dataset Summary

**Fintabnet-Logical** is a derivative of the original [FinTabNet](https://developer.adobe.com/document-services/docs/overview/pdf-extract-api/fintabnet-dataset/) dataset, specifically re-processed to create high-quality ground truth for logical table structure recognition (TSR).

While the original dataset provides cell content and HTML structure, this version parses that HTML to generate precise **logical coordinates** for every cell, correctly handling complex tables with `rowspan` and `colspan`. Furthermore, it processes the source PDFs to group text into **line-level cells**, assigning each line the logical coordinates of its parent cell.

The result is a clean, ready-to-use dataset for training models that predict not just the content of a table, but its fundamental logical grid structure. All table images are provided as high-resolution (144 DPI) crops for improved visual quality.

## Supported Tasks

* **Table Structure Recognition**: This dataset is primarily designed for training and evaluating models that recognize the logical row and column structure of tables, including row and column spans. The line-level cells with logical coordinates are ideal for this task.

## Dataset Structure

The dataset is organized into `train`, `val`, and `test` splits, mirroring the original FinTabNet. Each instance consists of a table image and a corresponding JSON annotation file.

### Data Instances

A typical annotation file (`.json`) has the following structure:

```json
{
    "fintabnet_annotations": { "... original fintabnet data ..." },
    "fintabnet_cells": [
        {
            "bbox": [187.0, 4.0, 261.0, 14.0],
            "tokens": ["...", "Practitioners", "..."],
            "logical_coords": [0, 0, 1, 5]
        }
    ],
    "word_cells": [
        {
            "text": "Practitioners",
            "bbox": [187.0, 4.0, 261.0, 14.0],
            "logical_coords": [0, 0, 1, 5]
        }
    ],
    "line_cells": [
        {
            "text": "General Practitioners",
            "bbox": [187.0, 4.0, 261.0, 14.0],
            "logical_coords": [0, 0, 1, 5]
        },
        {
            "text": "1. Antipsychotic drug treatment",
            "bbox": [4.0, 58.0, 133.0, 86.0],
            "logical_coords": [2, 2, 0, 0]
        }
    ]
}
```

### Data Fields

The most important key for training is `line_cells`:

* `line_cells`: A list of dictionaries, where each entry represents a single line of text within a table cell.
    * `text` (`str`): The text content of the line.
    * `bbox` (`list[float]`): The bounding box of the text line, in `[x_min, y_min, x_max, y_max]` format relative to the cropped table image.
    * `logical_coords` (`list[int]`): The logical coordinates of the parent cell in `[row_start, row_end, col_start, col_end]` format. An unspanned cell at the top-left would be `[0, 0, 0, 0]`. A cell spanning the first two rows in the first column would be `[0, 1, 0, 0]`.

### Data Splits

The dataset retains the original splits from FinTabNet:

| Split        | Number of Tables |
|--------------|------------------|
| **train** | 82,422           |
| **validation** | 9,539           |
| **test** | 9,599          |
| **Total** | **101,560** |

## Dataset Creation

### Curation Rationale

Many table recognition datasets provide only bounding boxes for cells, without the explicit logical row/column indices needed to understand the grid structure. This dataset was created to fill that gap. By parsing the HTML structure provided by FinTabNet, we generate a reliable ground truth for logical coordinates, which is invaluable for training and evaluating modern Table Structure Recognition models.

### Source Data

This dataset is derived from the **FinTabNet** dataset, which consists of tables from the annual financial reports of S&P 500 companies.

### Annotations

The annotation process is fully automated by a script that performs the following steps for each table:
1.  **Parse HTML**: The `structure` tokens from the original annotations are parsed to build a virtual grid of the table.
2.  **Calculate Logical Coordinates**: By traversing the virtual grid, the script calculates the `[row_start, row_end, col_start, col_end]` for every cell, accurately accounting for `rowspan` and `colspan` attributes.
3.  **Extract Words**: The source PDF is processed to extract all words and their bounding boxes within the table region.
4.  **Group into Lines**: Words are assigned to their parent cells based on spatial overlap. Within each cell, the words are grouped into lines based on reading order.
5.  **Assign Coordinates to Lines**: Each generated line is assigned the logical coordinates of its parent cell, creating the final `line_cells` ground truth.

---

## Citation

If you use this dataset, please cite the original FinTabNet paper:

```bibtex
@article{zheng2021global,
  title={Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context},
  author={Zheng, Xinyi and Burdick, Doug and Popa, Lucian and Sthankiya, Shachi and Teslee, Mitchell and Thomas, Bibin},
  journal={arXiv preprint arXiv:2109.04946},
  year={2021}
}
```