diff --git a/docling/models/layout_model.py b/docling/models/layout_model.py
index fbafdfc1..3ecb2648 100644
--- a/docling/models/layout_model.py
+++ b/docling/models/layout_model.py
@@ -189,6 +189,10 @@ class LayoutModel(BasePageModel):
                         np.mean([c.confidence for c in processed_clusters])
                     )
 
+                    conv_res.confidence.pages[page.page_no].ocr_score = float(
+                        np.mean([c.confidence for c in processed_cells if c.from_ocr])
+                    )
+
                     page.cells = processed_cells
                     page.predictions.layout = LayoutPrediction(
                         clusters=processed_clusters
diff --git a/docling/models/page_assemble_model.py b/docling/models/page_assemble_model.py
index a71ba8a9..a67cc1c2 100644
--- a/docling/models/page_assemble_model.py
+++ b/docling/models/page_assemble_model.py
@@ -160,13 +160,15 @@ class PageAssembleModel(BasePageModel):
 
                     # Aggregate page score
                     scores = conv_res.confidence.pages[page.page_no]
-                    scores.overall_score = float(np.nanmean(
-                        [
-                            scores.ocr_score,
-                            scores.table_score,
-                            scores.layout_score,
-                            scores.parse_score,
-                        ]
-                    ))
+                    scores.overall_score = float(
+                        np.nanmean(
+                            [
+                                scores.ocr_score,
+                                scores.table_score,
+                                scores.layout_score,
+                                scores.parse_score,
+                            ]
+                        )
+                    )
 
                 yield page
diff --git a/docling/models/page_preprocessing_model.py b/docling/models/page_preprocessing_model.py
index d1b29e38..5f901f65 100644
--- a/docling/models/page_preprocessing_model.py
+++ b/docling/models/page_preprocessing_model.py
@@ -1,10 +1,11 @@
 from pathlib import Path
 from typing import Iterable, Optional
 
+import numpy as np
 from PIL import ImageDraw
 from pydantic import BaseModel
 
-from docling.datamodel.base_models import Page
+from docling.datamodel.base_models import Page, ScoreValue
 from docling.datamodel.document import ConversionResult
 from docling.datamodel.settings import settings
 from docling.models.base_model import BasePageModel
@@ -59,6 +60,16 @@ class PagePreprocessingModel(BasePageModel):
         if self.options.create_parsed_page:
             page.parsed_page = page._backend.get_segmented_page()
 
+        # Rate the text quality from the PDF parser, and aggregate on page
+        text_scores = []
+        for c in page.cells:
+            score = self.rate_text_quality(c.text)
+            text_scores.append(score)
+
+        conv_res.confidence.pages[page.page_no].parse_score = float(
+            np.nanmean(text_scores)
+        )
+
         # DEBUG code:
         def draw_text_boxes(image, cells, show: bool = False):
             draw = ImageDraw.Draw(image)
@@ -87,3 +98,13 @@ class PagePreprocessingModel(BasePageModel):
             draw_text_boxes(page.get_image(scale=1.0), page.cells)
 
         return page
+
+    def rate_text_quality(self, text) -> ScoreValue:
+        """Rates the quality of a given text string by analyzing common PDF parsing issues."""
+
+        # Very poor-man rating function, must improve.
+        contains_glyph = text.find("GLYPH<") >= 0
+        if contains_glyph:
+            return 0.0
+
+        return 1.0