# FinixDocBench Markdown Evaluation

This folder contains a lightweight evaluator for FinixDocBench Markdown parsing outputs. It compares a directory of ground-truth `.md` files with a directory of predicted `.md` files whose file names match exactly.

The evaluator is intended for the public Markdown task. It does not evaluate structured JSON layout annotations.

## Metrics

The evaluator reports:

| Metric | Direction | Meaning |
|---|---|---|
| `text_block_Edit_dist` | Lower is better | Normalized edit distance over matched text blocks. |
| `reading_order_Edit_dist` | Lower is better | Normalized edit distance over serialized reading-order sequences. |
| `table_TEDS` | Higher is better | TEDS table-structure similarity, scaled to 0-100. |
| `overall` | Higher is better | Composite score on a 0-100 scale. |

The overall score is:

```python
overall = (
    (1 - text_block_Edit_dist) * 100
    + (1 - reading_order_Edit_dist) * 100
    + table_TEDS
) / 3
```

Formula parsing is not evaluated separately.

## Installation

Python 3.9+ is recommended.

```bash
cd FinixDocBench_Eval_for_Markdown
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
```

## Quick Check

Run the bundled minimal example first:

```bash
python run_eval.py \
  --gt_dir examples/gt \
  --pred_dir examples/pred \
  --output_json outputs/example_result.json
```

## Evaluate a FinixDocBench Track

Prepare a prediction directory with one `.md` file per evaluated page. Prediction file names must match the ground-truth Markdown file names.

Example for FinixPhoto:

```bash
python run_eval.py \
  --gt_dir ../track2_finixphoto_300/mds \
  --pred_dir /path/to/predicted_mds \
  --output_json outputs/finixphoto_result.json
```

Example for FinixHuge-Table:

```bash
python run_eval.py \
  --gt_dir ../track3_finixhuge_100_table/mds \
  --pred_dir /path/to/predicted_mds \
  --output_json outputs/finixhuge_table_result.json
```

## Output Format

The output JSON has the following structure:

```json
{
  "success": true,
  "metrics": {
    "text_block_Edit_dist": 0.0123,
    "reading_order_Edit_dist": 0.0,
    "table_TEDS": 98.7,
    "overall": 99.15,
    "num_samples": 2,
    "score": 99.15
  },
  "inputs": {
    "gt_files": 2,
    "pred_files": 2,
    "missing_predictions": 0,
    "unexpected_predictions": 0
  }
}
```

`score` is identical to `overall` and is included for leaderboard or automation systems that expect a generic score field.

## File Name Validation

By default, the evaluator fails if the `.md` file names in `gt_dir` and `pred_dir` do not match exactly. This avoids accidentally skipping pages.

If you want to allow missing predictions and score missing files as empty outputs, pass:

```bash
python run_eval.py \
  --gt_dir /path/to/gt_mds \
  --pred_dir /path/to/pred_mds \
  --allow_name_mismatch
```

## FinixHuge Reporting

For FinixHuge-Long and FinixHuge-Table, also report a success rate outside this script:

```text
success_rate = valid_non_empty_predictions / total_pages
```

A prediction should be counted as successful only if it is a syntactically valid, non-empty page-level Markdown result without runtime failure, severe truncation, or format errors that prevent downstream evaluation.

## Large Table Safeguards

TEDS can be slow on extremely large tables. To keep evaluation practical, this implementation assigns a table TEDS score of `0` when a ground-truth or predicted table exceeds `50000` `<td>` cells.

The matching stage also keeps broad safety thresholds:

```text
MAX_PRED_ITEMS = 50000
RATIO_THRESHOLD = 100
MAX_TOTAL_LENGTH = 10000000
MAX_SINGLE_ITEM_LENGTH = 10000000
```

## Notes

- Only `.md` files are evaluated.
- Images, JSON annotations, and other files are ignored by this evaluator.
- Markdown tables are converted to HTML before table evaluation.
- One page image should correspond to one Markdown file.
- File names are the matching keys; the evaluator does not read images.