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--- |
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license: other |
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pretty_name: "Fintabnet-Logical" |
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tags: |
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- table-structure-recognition |
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- table-understanding |
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- document-ai |
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- computer-vision |
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- finance |
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--- |
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# Fintabnet-Logical |
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## Dataset Summary |
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**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). |
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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. |
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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. |
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## Supported Tasks |
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* **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. |
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## Dataset Structure |
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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. |
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### Data Instances |
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A typical annotation file (`.json`) has the following structure: |
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```json |
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{ |
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"fintabnet_annotations": { "... original fintabnet data ..." }, |
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"fintabnet_cells": [ |
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{ |
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"bbox": [187.0, 4.0, 261.0, 14.0], |
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"tokens": ["...", "Practitioners", "..."], |
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"logical_coords": [0, 0, 1, 5] |
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} |
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], |
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"word_cells": [ |
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{ |
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"text": "Practitioners", |
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"bbox": [187.0, 4.0, 261.0, 14.0], |
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"logical_coords": [0, 0, 1, 5] |
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} |
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], |
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"line_cells": [ |
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{ |
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"text": "General Practitioners", |
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"bbox": [187.0, 4.0, 261.0, 14.0], |
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"logical_coords": [0, 0, 1, 5] |
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}, |
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{ |
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"text": "1. Antipsychotic drug treatment", |
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"bbox": [4.0, 58.0, 133.0, 86.0], |
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"logical_coords": [2, 2, 0, 0] |
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} |
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] |
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} |
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``` |
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### Data Fields |
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The most important key for training is `line_cells`: |
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* `line_cells`: A list of dictionaries, where each entry represents a single line of text within a table cell. |
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* `text` (`str`): The text content of the line. |
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* `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. |
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* `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]`. |
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### Data Splits |
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The dataset retains the original splits from FinTabNet: |
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| Split | Number of Tables | |
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|--------------|------------------| |
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| **train** | 82,422 | |
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| **validation** | 9,539 | |
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| **test** | 9,599 | |
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| **Total** | **101,560** | |
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## Dataset Creation |
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### Curation Rationale |
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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. |
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### Source Data |
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This dataset is derived from the **FinTabNet** dataset, which consists of tables from the annual financial reports of S&P 500 companies. |
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### Annotations |
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The annotation process is fully automated by a script that performs the following steps for each table: |
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1. **Parse HTML**: The `structure` tokens from the original annotations are parsed to build a virtual grid of the table. |
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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. |
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3. **Extract Words**: The source PDF is processed to extract all words and their bounding boxes within the table region. |
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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. |
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5. **Assign Coordinates to Lines**: Each generated line is assigned the logical coordinates of its parent cell, creating the final `line_cells` ground truth. |
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--- |
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## Citation |
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If you use this dataset, please cite the original FinTabNet paper: |
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```bibtex |
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@article{zheng2021global, |
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title={Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context}, |
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author={Zheng, Xinyi and Burdick, Doug and Popa, Lucian and Sthankiya, Shachi and Teslee, Mitchell and Thomas, Bibin}, |
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journal={arXiv preprint arXiv:2109.04946}, |
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year={2021} |
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} |
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``` |
