Docling metrics for document tables

Docling metrics for document tables.

Overview

The following metrics are used:

  • Tree Edit Distance.
  • GriTS (coming).
**Directory structure:** Before building the C++ code, we have only the source code directories: 
.
├── devtools              # Various bash scripts, useful with C++ development
├── cmake                 # Cmake files required to compile the C++ code.
├── cpp_src               # C++ source code
├── cpp_tests             # C++ source code for the test
├── docling_metrics_table   # Python wrapper for the C++ bindings that implements the docling-metrics-core API
└── test                  # Python tests

After building the C++ code we have the following directories:

.
├── build                  # C++ build directory required during the build process
├── cmake
├── cpp_src
├── cpp_test
├── docling_metrics_table
├── externals              # C++ code of external libraries. Required during the compilation.
└── tests

Installation

All C++ and Python code is managed by uv. The following command starts the workflow that:

  1. Installs all Python dependencies.
  2. Builds the C++ code including the C++ external dependencies.
  3. Installs the *.so file with the python bindings inside the uv venv.
uv sync --all-packages

In case a manual compilation of the C++ code is needed, you can use the bash scripts from devtools/:

Build the C++ code:

devtools/build_cpp.sh

Run the native C++ tests:

devtools/test_cpp.sh

Usage

from docling_metrics_table import (
    TableMetric,
    TableMetricBracketInputSample,
    TableMetricHTMLInputSample,
    TableMetricSampleEvaluation,
)

# Input sample in bracket notation
bracket_sample = TableMetricBracketInputSample(
    id="s1",
    bracket_a="{x{a}{b}}",
    bracket_b="{x{a}{c}}",
)

table_metric = TableMetric()
bracket_sample_evaluation: TableMetricSampleEvaluation = table_metric.evaluate_sample(
    bracket_sample
)
print(f"TEDS with bracket input: {bracket_sample_evaluation}")


# Input sample in HTML notation
html_a = r"""
<table>
    <tr>
        <td colspan="2">This cell spans two columns with some dummy text</td>
    </tr>
    <tr>
        <td>Cell 2-1: More dummy text here</td>
        <td>Cell 2-2: Additional content</td>
    </tr>
</table>
"""

html_b = r"""
<table>
    <tr>
        <td>Dummy text</td>
        <td>Cell 1-2: Regular cell content</td>
    </tr>
    <tr>
        <td>Cell 2-1: More dummy text here</td>
        <td>Cell 2-2: Additional content</td>
    </tr>
</table>
"""

html_sample = TableMetricHTMLInputSample(
    id="s1",
    html_a=html_a,
    html_b=html_b,
    structure_only=False,
)
html_evaluation: TableMetricSampleEvaluation = table_metric.evaluate_sample(html_sample)
print(f"TEDS with HTML input: {html_evaluation}")

Links

tree-similarity

License

MIT

Project links

Data verified by PyPI on Aug 13, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

Owner

PyPI data
Data sourced directly from PyPI's database.

Credits

Author: Nikos Livathinos

GitHub Statistics

Data verified by PyPI on Aug 13, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python <4.0, >=3.10

Tags

docling metrics evaluation example table teds tree-edit-distance-score cpp

Classifiers

Report project as malware

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