diff --git a/document_preview/document_preview.py b/document_preview/document_preview.py
index 6928316..b467fb2 100644
--- a/document_preview/document_preview.py
+++ b/document_preview/document_preview.py
@@ -281,16 +281,38 @@ def execute(self, request):
         run_ocr_on_first_n_pages = request.get_param("run_ocr_on_first_n_pages")
         previews = [s for s in os.listdir(self.working_directory) if "output" in s]
 
-        if not run_ocr_on_first_n_pages:
-            # Add all images to section (no need to run OCR)
+        def attach_images_to_section(run_ocr=False) -> str:
+            extracted_text = ""
             for i, preview in enumerate(natsorted(previews)):
+                ocr_heur_id, ocr_io = None, None
+                if run_ocr:
+                    # Trigger OCR on the first N pages as specified in the submission
+                    ocr_heur_id = 1 if request.deep_scan or (i < run_ocr_on_first_n_pages) else None
+                    ocr_io = StringIO()
+
                 img_name = f"page_{str(i).zfill(3)}.png"
                 fp = os.path.join(self.working_directory, preview)
                 image_section.add_image(
                     fp,
                     name=img_name,
                     description=f"Here's the preview for page {i}",
+                    ocr_heuristic_id=ocr_heur_id,
+                    ocr_io=ocr_io,
                 )
+
+                if request.get_param("analyze_render"):
+                    request.add_extracted(
+                        fp,
+                        name=img_name,
+                        description=f"Here's the preview for page {i}",
+                    )
+                if run_ocr:
+                    extracted_text += f"{ocr_io.read()}\n\n"
+            return extracted_text
+
+        if not run_ocr_on_first_n_pages:
+            # Add all images to section (no need to run OCR)
+            attach_images_to_section()
         else:
             # If we have a PDF at our disposal,
             # try to extract the text from that rather than relying on OCR for everything
@@ -299,21 +321,7 @@ def execute(self, request):
             if extracted_text_path is not None:
                 extracted_text = open(extracted_text_path, "r").read()
                 # Add all images to section
-                for i, preview in enumerate(natsorted(previews)):
-                    img_name = f"page_{str(i).zfill(3)}.png"
-                    fp = os.path.join(self.working_directory, preview)
-                    image_section.add_image(
-                        fp,
-                        name=img_name,
-                        description=f"Here's the preview for page {i}",
-                    )
-
-                    if request.get_param("analyze_render"):
-                        request.add_extracted(
-                            fp,
-                            name=img_name,
-                            description=f"Here's the preview for page {i}",
-                        )
+                attach_images_to_section()
 
                 # We were able to extract content, perform term detection
                 detections = indicator_detections(extracted_text)
@@ -343,28 +351,7 @@ def execute(self, request):
                     image_section.add_subsection(ocr_section)
             else:
                 # Unable to extract text from PDF, run it through Tesseract for term detection
-                for i, preview in enumerate(natsorted(previews)):
-                    # Trigger OCR on the first N pages as specified in the submission
-                    # Otherwise, just add the image without performing OCR analysis
-                    ocr_heur_id = 1 if request.deep_scan or (i < run_ocr_on_first_n_pages) else None
-                    ocr_io = StringIO()
-                    img_name = f"page_{str(i).zfill(3)}.png"
-                    image_section.add_image(
-                        f"{self.working_directory}/{preview}",
-                        name=img_name,
-                        description=f"Here's the preview for page {i}",
-                        ocr_heuristic_id=ocr_heur_id,
-                        ocr_io=ocr_io,
-                    )
-
-                    if request.get_param("analyze_render"):
-                        request.add_extracted(
-                            f"{self.working_directory}/{preview}",
-                            name=img_name,
-                            description=f"Here's the preview for page {i}",
-                        )
-
-                    extracted_text += f"{ocr_io.read()}\n\n"
+                extracted_text += attach_images_to_section(run_ocr=True)
 
             # Tag any network IOCs found in OCR output
             self.tag_network_iocs(image_section, extracted_text)