diff --git a/docling/pipeline/threaded_standard_pdf_pipeline.py b/docling/pipeline/threaded_standard_pdf_pipeline.py
index 99e83770..a31270d0 100644
--- a/docling/pipeline/threaded_standard_pdf_pipeline.py
+++ b/docling/pipeline/threaded_standard_pdf_pipeline.py
@@ -20,10 +20,14 @@ import itertools
 import logging
 import threading
 import time
+import warnings
 from collections import defaultdict, deque
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any, Iterable, List, Optional, Sequence, Tuple
+from typing import Any, Iterable, List, Optional, Sequence, Tuple, cast
+
+import numpy as np
+from docling_core.types.doc import DocItem, ImageRef, PictureItem, TableItem
 
 from docling.backend.abstract_backend import AbstractDocumentBackend
 from docling.backend.pdf_backend import PdfDocumentBackend
@@ -541,6 +545,86 @@ class ThreadedStandardPdfPipeline(ConvertPipeline):
                 elements=elements, headers=headers, body=body
             )
             conv_res.document = self.reading_order_model(conv_res)
+
+            # Generate page images in the output
+            if self.pipeline_options.generate_page_images:
+                for page in conv_res.pages:
+                    assert page.image is not None
+                    page_no = page.page_no + 1
+                    conv_res.document.pages[page_no].image = ImageRef.from_pil(
+                        page.image, dpi=int(72 * self.pipeline_options.images_scale)
+                    )
+
+            # Generate images of the requested element types
+            with warnings.catch_warnings():  # deprecated generate_table_images
+                warnings.filterwarnings("ignore", category=DeprecationWarning)
+                if (
+                    self.pipeline_options.generate_picture_images
+                    or self.pipeline_options.generate_table_images
+                ):
+                    scale = self.pipeline_options.images_scale
+                    for element, _level in conv_res.document.iterate_items():
+                        if not isinstance(element, DocItem) or len(element.prov) == 0:
+                            continue
+                        if (
+                            isinstance(element, PictureItem)
+                            and self.pipeline_options.generate_picture_images
+                        ) or (
+                            isinstance(element, TableItem)
+                            and self.pipeline_options.generate_table_images
+                        ):
+                            page_ix = element.prov[0].page_no - 1
+                            page = next(
+                                (p for p in conv_res.pages if p.page_no == page_ix),
+                                cast("Page", None),
+                            )
+                            assert page is not None
+                            assert page.size is not None
+                            assert page.image is not None
+
+                            crop_bbox = (
+                                element.prov[0]
+                                .bbox.scaled(scale=scale)
+                                .to_top_left_origin(
+                                    page_height=page.size.height * scale
+                                )
+                            )
+
+                            cropped_im = page.image.crop(crop_bbox.as_tuple())
+                            element.image = ImageRef.from_pil(
+                                cropped_im, dpi=int(72 * scale)
+                            )
+
+            # Aggregate confidence values for document:
+            if len(conv_res.pages) > 0:
+                with warnings.catch_warnings():
+                    warnings.filterwarnings(
+                        "ignore",
+                        category=RuntimeWarning,
+                        message="Mean of empty slice|All-NaN slice encountered",
+                    )
+                    conv_res.confidence.layout_score = float(
+                        np.nanmean(
+                            [c.layout_score for c in conv_res.confidence.pages.values()]
+                        )
+                    )
+                    conv_res.confidence.parse_score = float(
+                        np.nanquantile(
+                            [c.parse_score for c in conv_res.confidence.pages.values()],
+                            q=0.1,  # parse score should relate to worst 10% of pages.
+                        )
+                    )
+                    conv_res.confidence.table_score = float(
+                        np.nanmean(
+                            [c.table_score for c in conv_res.confidence.pages.values()]
+                        )
+                    )
+                    conv_res.confidence.ocr_score = float(
+                        np.nanmean(
+                            [c.ocr_score for c in conv_res.confidence.pages.values()]
+                        )
+                    )
+
         return conv_res
 
     # ---------------------------------------------------------------- misc