| | --- |
| | license: mit |
| | task_categories: |
| | - table-question-answering |
| | - image-to-text |
| | tags: |
| | - table-extraction |
| | - benchmark |
| | - fintabnet |
| | - document-ai |
| | - docld |
| | pretty_name: DocLD FinTabNet Benchmark |
| | size_categories: |
| | - n<1K |
| | --- |
| | |
| | # DocLD FinTabNet Benchmark Results |
| |
|
| | Benchmark results for [DocLD](https://docld.com) table extraction on the |
| | [FinTabNet](https://paperswithcode.com/dataset/fintabnet) dataset. |
| |
|
| | ## Results Summary |
| |
|
| | | Metric | Value | |
| | |--------|-------| |
| | | **Mean Accuracy** | 82.1% | |
| | | **Median** | 83.2% | |
| | | **P25 / P75** | 73.3% / 97.4% | |
| | | **Min / Max** | 22.7% / 100.0% | |
| | | **Scored Samples** | 500 | |
| | | **Total Samples** | 500 | |
| |
|
| | ## Methodology |
| |
|
| | - **Dataset**: [FinTabNet_OTSL](https://huggingface.co/datasets/docling-project/FinTabNet_OTSL) — 500 samples from the test split |
| | - **Extraction**: DocLD vision-based table extraction |
| | - **Scoring**: Needleman-Wunsch hierarchical alignment (same as [RD-TableBench](https://github.com/reductoai/rd-tablebench)) |
| | - **Output**: HTML tables with rowspan/colspan for merged cells |
| |
|
| | ## Comparison |
| |
|
| | | Provider | FinTabNet Accuracy | |
| | |----------|-------------------| |
| | | **DocLD** | **82.1%** | |
| | | GTE (IBM) | ~78% | |
| | | TATR (Microsoft) | ~65% | |
| |
|
| | ## Files |
| |
|
| | - `results.json` — Full benchmark results with per-sample scores |
| | - `predictions/` — HTML predictions for each sample |
| | - `charts/` — Visualization PNGs |
| |
|
| | ## Links |
| |
|
| | - [DocLD](https://docld.com) |
| | - [Blog Post](https://docld.com/blog/docld-fintabnet) |
| | - [Benchmark Code](https://github.com/Doc-LD/fintabnet-bench) |
| | - [RD-TableBench Results](https://docld.com/blog/docld-tablebench) |
| |
|
