diff --git a/tests/test_ocr_docling.py b/tests/test_ocr_docling.py
new file mode 100644
index 00000000..1b6f47c3
--- /dev/null
+++ b/tests/test_ocr_docling.py
@@ -0,0 +1,98 @@
+import logging
+import multiprocessing
+import os
+from pathlib import Path
+
+import pytest
+from docling.datamodel.base_models import InputFormat
+from docling.datamodel.pipeline_options import (
+    AcceleratorOptions,
+    OcrOptions,
+    PdfPipelineOptions,
+)
+from docling.document_converter import PdfFormatOption
+from docling.models.factories import get_ocr_factory
+
+from docling_eval.cli.main import evaluate, visualize
+from docling_eval.datamodels.types import BenchMarkNames, EvaluationModality
+from docling_eval.dataset_builders.xfund_builder import XFUNDDatasetBuilder
+from docling_eval.prediction_providers.docling_provider import DoclingPredictionProvider
+
+IS_CI = os.getenv("RUN_IN_CI") == "1"
+
+logging.getLogger("matplotlib").setLevel(logging.WARNING)
+logging.getLogger("urllib3").setLevel(logging.WARNING)
+logging.getLogger("PIL").setLevel(logging.WARNING)
+logging.getLogger("filelock").setLevel(logging.WARNING)
+
+
+@pytest.mark.skipif(
+    IS_CI, reason="Skipping test in CI because the dataset is too heavy."
+)
+def test_run_xfund_builder_docling():
+    target_path = Path(f"./scratch/{BenchMarkNames.XFUND.value}_docling/")
+
+    ocr_factory = get_ocr_factory()
+    ocr_options: OcrOptions = ocr_factory.create_options(
+        kind="easyocr",
+    )
+    ocr_options.use_gpu = False
+
+    accelerator_options = AcceleratorOptions(
+        num_threads=multiprocessing.cpu_count(),
+    )
+
+    pipeline_options = PdfPipelineOptions(
+        do_ocr=True,
+        do_table_structure=False,
+        ocr_options=ocr_options,
+        accelerator_options=accelerator_options,
+        images_scale=2.0,
+        generate_page_images=True,
+        generate_picture_images=True,
+        generate_parsed_pages=True,
+    )
+
+    prediction_provider = DoclingPredictionProvider(
+        format_options={
+            InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options),
+            InputFormat.IMAGE: PdfFormatOption(pipeline_options=pipeline_options),
+        },
+        do_visualization=False,
+        ignore_missing_predictions=True,
+    )
+
+    dataset = XFUNDDatasetBuilder(
+        dataset_source=target_path / "input_dataset",
+        target=target_path / "gt_dataset",
+        end_index=1,
+    )
+
+    dataset.retrieve_input_dataset()
+    dataset.save_to_disk()
+
+    prediction_provider.create_prediction_dataset(
+        name=dataset.name,
+        gt_dataset_dir=target_path / "gt_dataset",
+        target_dataset_dir=target_path / "eval_dataset",
+        split="val",
+        begin_index=0,
+        end_index=2,
+        chunk_size=5,
+    )
+
+    evaluate(
+        modality=EvaluationModality.OCR,
+        benchmark=BenchMarkNames.XFUND,
+        idir=target_path / "eval_dataset",
+        odir=target_path / "evaluations" / EvaluationModality.OCR.value,
+        split="val",
+    )
+
+    visualize(
+        modality=EvaluationModality.OCR,
+        benchmark=BenchMarkNames.XFUND,
+        idir=target_path / "eval_dataset",
+        odir=target_path / "evaluations" / EvaluationModality.OCR.value,
+        split="val",
+    )