microsoft · Paul Lizer (paullizer) · Aug 18, 2026 · Aug 18, 2026
diff --git a/application/single_app/config.py b/application/single_app/config.py
@@ -96,7 +96,7 @@
 EXECUTOR_TYPE = 'thread'
 EXECUTOR_MAX_WORKERS = 30
 SESSION_TYPE = 'filesystem'
-VERSION = "0.250.223"
+VERSION = "0.250.224"
 IS_DEVELOPMENT = is_development_env_enabled()
 
 SESSION_COOKIE_SAMESITE = os.getenv('SESSION_COOKIE_SAMESITE', 'Lax')

diff --git a/application/single_app/functions_content.py b/application/single_app/functions_content.py
@@ -10,6 +10,7 @@
 
 from functions_debug import debug_print
 from config import *
+from azure.ai.documentintelligence.models import DocumentAnalysisFeature
 from functions_office_media import extract_office_embedded_images
 import functions_settings
 from functions_settings import *
@@ -453,6 +454,11 @@ def extract_content_with_azure_di(file_path, extraction_mode='read', pages=None)
             analyze_options["output_content_format"] = "markdown"
         if pages:
             analyze_options["pages"] = str(pages)
+
+        # Formula extraction is a billed Document Intelligence add-on, so it is opt-in and only
+        # applies to Layout, which is the model that supports it.
+        if normalized_extraction_mode == "layout" and functions_settings.is_document_intelligence_formula_extraction_enabled():
+            analyze_options["features"] = [DocumentAnalysisFeature.FORMULAS]
 
         # Debug logging for troubleshooting
         debug_print(f"Starting Azure DI extraction for: {os.path.basename(file_path)}")

diff --git a/application/single_app/functions_documents.py b/application/single_app/functions_documents.py
@@ -7498,10 +7498,11 @@
                  final_chunks_to_save = di_extracted_pages
             else: final_chunks_to_save = [] # No text extracted
 
-        # --- Embedded Office image analysis (DOCX/DOC/PPTX) ---
+        # --- Embedded Office image analysis (DOCX/DOC/PPTX/PPT) ---
         # Neither extraction engine describes figures inside Office files, so embedded images are
-        # analyzed separately and appended as their own citable chunks.
-        if (is_word or is_ppt) and not is_legacy_doc and not is_legacy_ppt:
+        # analyzed separately and appended as their own citable chunks. Legacy binary formats are
+        # included because their pictures are carved from the OLE container by signature.
+        if is_word or is_ppt:
             next_chunk_page_number = max(
                 (int(chunk.get('page_number') or 0) for chunk in final_chunks_to_save),
                 default=0,

diff --git a/application/single_app/functions_office_media.py b/application/single_app/functions_office_media.py
@@ -12,6 +12,7 @@
 import hashlib
 import os
 import re
+import struct
 import zipfile
 from io import BytesIO
 
@@ -46,6 +47,9 @@
 OFFICE_ZIP_MAX_RELS_ENTRIES = 500
 # Only the compression methods real OOXML packages use.
 OFFICE_ZIP_ALLOWED_COMPRESSION = (zipfile.ZIP_STORED, zipfile.ZIP_DEFLATED)
+# Legacy .doc/.ppt are OLE compound documents, so their pictures are carved by signature instead.
+OLE_COMPOUND_FILE_SIGNATURE = b'\xd0\xcf\x11\xe0\xa1\xb1\x1a\xe1'
+OFFICE_BINARY_SCAN_MAX_BYTES = 256 * 1024 * 1024
 
 _OFFICE_MEDIA_INDEX_PATTERN = re.compile(r'(\d+)')
 _PPTX_SLIDE_RELS_PATTERN = re.compile(r'^ppt/slides/_rels/slide(\d+)\.xml\.rels$', re.IGNORECASE)
@@ -200,6 +204,130 @@
     return extracted_images
 
 
+def _carve_metafiles_from_binary(data, max_metafiles):
+    """Locate EMF and placeable WMF blobs inside a non-zip Office container.
+
+    Legacy ``.doc`` and ``.ppt`` files are OLE compound documents rather than zip packages, so
+    there is no ``word/media`` part to enumerate. Their pictures and embedded equation previews are
+    still stored as intact metafile blobs, which can be located by signature and carved using the
+    length recorded in the metafile's own header.
+
+    Validation is deliberately strict -- record type, signature position, and a length that fits
+    inside the remaining bytes -- so a coincidental byte sequence is not mistaken for an image.
+    """
+    carved = []
+    offset = 0
+    length = len(data)
+
+    while offset < length and len(carved) < max_metafiles:
+        emf_index = data.find(b' EMF', offset)
+        wmf_index = data.find(b'\xd7\xcd\xc6\x9a', offset)
+
+        candidates = [index for index in (emf_index, wmf_index) if index != -1]
+        if not candidates:
+            break
+        next_index = min(candidates)
+
+        if next_index == emf_index:
+            record_start = emf_index - 40
+            offset = emf_index + 4
+            if record_start < 0 or record_start + 88 > length:
+                continue
+            record_type, _record_size = struct.unpack_from('<II', data, record_start)
+            if record_type != 1:
+                continue
+            total_bytes = struct.unpack_from('<I', data, record_start + 48)[0]
+            if not (88 <= total_bytes <= OFFICE_EMBEDDED_IMAGE_MAX_BYTES):
+                continue
+            if record_start + total_bytes > length:
+                continue
+            carved.append(('emf', data[record_start:record_start + total_bytes]))
+            offset = record_start + total_bytes
+        else:
+            record_start = wmf_index
+            offset = wmf_index + 4
+            if record_start + 30 > length:
+                continue
+            # Placeable header is 22 bytes; the standard header's mtSize is measured in words.
+            size_words = struct.unpack_from('<I', data, record_start + 28)[0]
+            total_bytes = 22 + size_words * 2
+            if not (30 <= total_bytes <= OFFICE_EMBEDDED_IMAGE_MAX_BYTES):
+                continue
+            if record_start + total_bytes > length:
+                continue
+            carved.append(('wmf', data[record_start:record_start + total_bytes]))
+            offset = record_start + total_bytes
+
+    return carved
+
+
+def _extract_from_binary_office_file(file_path, output_dir, min_pixels, max_images, diagnostics):
+    """Extract embedded metafiles from a legacy binary Office document."""
+    try:
+        file_size = os.path.getsize(file_path)
+        if file_size > OFFICE_BINARY_SCAN_MAX_BYTES:
+            return []
+        with open(file_path, 'rb') as handle:
+            data = handle.read()
+    except OSError:
+        return []
+
+    if not data.startswith(OLE_COMPOUND_FILE_SIGNATURE):
+        return []
+
+    carved = _carve_metafiles_from_binary(data, max_images * 4)
+    diagnostics['candidates'] = len(carved)
+
+    extracted_images = []
+    seen_digests = set()
+
+    for source_format, blob in carved:
+        if len(extracted_images) >= max_images:
+            _record_skip(diagnostics, 'per_document_cap_reached')
+            continue
+
+        if len(blob) < OFFICE_EMBEDDED_IMAGE_MIN_BYTES:
+            _record_skip(diagnostics, 'below_minimum_bytes')
+            continue
+
+        digest = hashlib.sha256(blob).hexdigest()
+        if digest in seen_digests:
+            _record_skip(diagnostics, 'duplicate_image')
+            continue
+
+        png_bytes, width, height, embedded_text, reason = _rasterize_vector_image(blob)
+        if png_bytes is None:
+            _record_skip(diagnostics, reason or 'vector_not_rasterizable')
+            continue
+
+        if width < min_pixels or height < min_pixels:
+            _record_skip(diagnostics, 'below_minimum_pixels')
+            continue
+
+        seen_digests.add(digest)
+        output_path = os.path.join(output_dir, f"{len(extracted_images) + 1:03d}.png")
+        try:
+            with open(output_path, 'wb') as output_file:
+                output_file.write(png_bytes)
+        except OSError:
+            _record_skip(diagnostics, 'write_failed')
+            continue
+
+        diagnostics['analyzed'] += 1
+        extracted_images.append({
+            'name': f"embedded_{len(extracted_images) + 1}.{source_format}",
+            'path': output_path,
+            'width': width,
+            'height': height,
+            'slide_number': None,
+            'source_format': source_format,
+            'rasterized': True,
+            'embedded_text': embedded_text,
+        })
+
+    return extracted_images
+
+
 def extract_office_embedded_images_with_diagnostics(file_path, output_dir, min_pixels=150, max_images=25):
     """Extract embedded images and report what was skipped and why.
 
@@ -216,6 +344,15 @@
     if max_images <= 0:
         return [], diagnostics
 
+    # Legacy .doc and .ppt are OLE compound documents, not zip packages.
+    if not zipfile.is_zipfile(file_path):
+        try:
+            return _extract_from_binary_office_file(
+                file_path, output_dir, min_pixels, max_images, diagnostics
+            ), diagnostics
+        except (OSError, ValueError, struct.error):
+            return [], diagnostics
+
     extracted_images = []
     seen_digests = set()
 

diff --git a/application/single_app/functions_settings.py b/application/single_app/functions_settings.py
@@ -866,6 +866,15 @@ def normalize_extraction_engine(value):
     return normalized_value
 
 
+def is_document_intelligence_formula_extraction_enabled(settings=None):
+    """Return whether the Document Intelligence formula add-on should be requested.
+
+    Formula extraction is a billed add-on, so it defaults to off and must be enabled explicitly.
+    """
+    resolved_settings = settings if settings is not None else get_settings()
+    return bool((resolved_settings or {}).get('enable_document_intelligence_formula_extraction', False))
+
+
 def normalize_app_role_claims(user_roles):
     """Normalize app role claims into a flat string list."""
     if not user_roles:
@@ -1727,6 +1736,8 @@ def get_settings(use_cosmos=False, include_source=False):
         'azure_document_intelligence_authentication_type': 'key',
         'document_intelligence_pdf_image_extraction_mode': 'read',
         'document_intelligence_auto_sample_pages': DOCUMENT_INTELLIGENCE_AUTO_SAMPLE_PAGES_DEFAULT,
+        # Formula extraction is a billed Document Intelligence add-on, so it stays off by default.
+        'enable_document_intelligence_formula_extraction': False,
         'enable_document_intelligence_apim': False,
         'azure_apim_document_intelligence_endpoint': '',
         'azure_apim_document_intelligence_subscription_key': '',

diff --git a/application/single_app/route_frontend_admin_settings.py b/application/single_app/route_frontend_admin_settings.py
@@ -2761,6 +2761,7 @@ def is_valid_url(url):
                 'azure_document_intelligence_authentication_type': form_data.get('azure_document_intelligence_authentication_type', 'key'),
                 'document_intelligence_pdf_image_extraction_mode': document_intelligence_pdf_image_extraction_mode,
                 'document_intelligence_auto_sample_pages': document_intelligence_auto_sample_pages,
+                'enable_document_intelligence_formula_extraction': form_data.get('enable_document_intelligence_formula_extraction') == 'on',
                 'enable_document_intelligence_apim': form_data.get('enable_document_intelligence_apim') == 'on',
                 'azure_apim_document_intelligence_endpoint': form_data.get('azure_apim_document_intelligence_endpoint', '').strip(),
                 'azure_apim_document_intelligence_subscription_key': admin_secret('azure_apim_document_intelligence_subscription_key'),

diff --git a/application/single_app/static/js/admin/admin_settings.js b/application/single_app/static/js/admin/admin_settings.js
@@ -10552,6 +10552,7 @@ function setupWalkthroughFieldListeners() {
             {selector: '#azure_content_understanding_analyzer_id', event: 'input'},
             {selector: '#azure_content_understanding_image_analyzer_id', event: 'input'},
             {selector: '#enable_office_embedded_image_analysis', event: 'change'},
+            {selector: '#enable_document_intelligence_formula_extraction', event: 'change'},
             {selector: '#office_embedded_image_min_pixels', event: 'input'},
             {selector: '#office_embedded_image_max_per_document', event: 'input'}
         ],

diff --git a/application/single_app/templates/admin_settings.html b/application/single_app/templates/admin_settings.html
@@ -11798,6 +11798,26 @@ <h5><i class="bi bi-file-earmark-text me-2"></i>Document Intelligence</h5>
                             </div>
                         </div>
 
+                        <div class="form-group form-check form-switch mb-3 d-flex align-items-center">
+                            <input
+                                type="checkbox"
+                                class="form-check-input me-2"
+                                id="enable_document_intelligence_formula_extraction"
+                                name="enable_document_intelligence_formula_extraction"
+                                {% if settings.enable_document_intelligence_formula_extraction %}checked{% endif %}
+                            >
+                            <label class="form-check-label" for="enable_document_intelligence_formula_extraction">
+                                Extract mathematical formulas
+                            </label>
+                            <i class="bi bi-info-circle ms-2" data-bs-toggle="tooltip" title="Requests the Document Intelligence formulas add-on so equations are captured as LaTeX instead of approximated as OCR text."></i>
+                        </div>
+                        <div class="form-text mb-3">
+                            Captures equations in PDFs and images as LaTeX rather than approximate OCR text. This is a
+                            <strong>billed Document Intelligence add-on</strong> that adds per-page cost to every Enhanced
+                            extraction, so it is off by default. It applies to the Layout model only, so it has no effect
+                            while extraction is set to Standard.
+                        </div>
+
                         {% if content_understanding_supported %}
                         <div class="card p-3 mb-3 bg-body-tertiary" id="content-understanding-section">
                             <div class="d-flex align-items-center gap-2 flex-wrap mb-2">

diff --git a/docs/explanation/features/CONTENT_UNDERSTANDING_ENHANCED_EXTRACTION.md b/docs/explanation/features/CONTENT_UNDERSTANDING_ENHANCED_EXTRACTION.md
@@ -94,6 +94,7 @@ All settings live in **Admin Settings → Extract**.
 | Enable Enhanced extraction | `enable_enhanced_extraction` | `False` |
 | PDF and Image Extraction Mode | `document_intelligence_pdf_image_extraction_mode` | `read` |
 | Auto Sample Pages | `document_intelligence_auto_sample_pages` | `3` |
+| Extract mathematical formulas | `enable_document_intelligence_formula_extraction` | `False` |
 | Content Understanding Endpoint | `azure_content_understanding_endpoint` | `""` |
 | Authentication Type | `azure_content_understanding_authentication_type` | `key` |
 | Content Understanding Key | `azure_content_understanding_key` | `""` |
@@ -175,7 +176,10 @@ them separately.
   `ppt/slides/_rels/slideN.xml.rels`.
 - Each analyzed image becomes its own citable chunk with a heading such as
   `### Embedded image 2 of 5: image2.png on slide 3`.
-- Legacy `.doc` and `.ppt` files are OLE containers rather than zip packages, so they are skipped.
+- Legacy `.doc` and `.ppt` files are OLE compound documents rather than zip packages, so their
+  pictures are carved out by metafile signature instead of being enumerated from media parts. The
+  carve validates the record type, signature position, and declared length before accepting a blob,
+  so a coincidental byte sequence is not mistaken for an image.
 
 Embedded image analysis never fails a document. Individual image failures are logged and skipped.
 
@@ -284,9 +288,13 @@ Enhanced extraction is disabled.
 - There is no APIM passthrough option for Content Understanding yet.
 - Content Understanding markdown is denser than Document Intelligence Read output because it
   includes HTML tables and figure descriptions, so chunks are larger.
-- Legacy `.doc` and `.ppt` files do not support embedded image analysis.
 - Metafile rasterization covers the drawing records Office emits for diagrams. Gradients, complex
   clipping regions, and embedded bitmap blits inside a metafile are not reproduced, and text is
   drawn with a default font rather than the original typeface, so a rasterized diagram is a
   faithful-enough likeness rather than an exact reproduction. The text drawn inside the metafile is
   extracted separately and attached to the chunk, so labels remain accurate regardless.
+- Equations authored in modern Word are stored as OMML markup rather than images, so they are not
+  covered by embedded image analysis. Legacy Equation Editor and MathType objects are stored with a
+  metafile preview and are covered.
+- Formula extraction requires the billed Document Intelligence add-on and applies to the Layout
+  model only, so it has no effect while extraction is set to Standard.
diff --git a/docs/explanation/release_notes.md b/docs/explanation/release_notes.md
@@ -2,6 +2,23 @@
 
 For feature-focused and fix-focused drill-downs by version, see [Features by Version](/explanation/features/) and [Fixes by Version](/explanation/fixes/).
 
+### **(v0.250.224)**
+
+#### New Features
+
+*   **Optional Mathematical Formula Extraction**
+    *   Added an **Extract mathematical formulas** toggle to the Document Intelligence settings. When enabled, equations in PDFs and images are captured as LaTeX instead of being approximated as OCR text.
+    *   This requests a **billed Document Intelligence add-on**, so it is off by default and must be turned on deliberately. It applies to the Layout model only, so it has no effect while extraction is set to Standard.
+    *   (Ref: #1277, `functions_content.py`, `functions_settings.py`, `admin_settings.html`, Document Intelligence formulas add-on)
+
+#### Bug Fixes
+
+*   **Images in Legacy `.doc` and `.ppt` Files Are Now Analyzed**
+    *   Embedded image analysis previously covered only DOCX and PPTX, because legacy Office files are OLE compound documents rather than zip packages and have no media parts to enumerate.
+    *   Pictures and embedded equation previews are now carved out of the legacy container by metafile signature, using the length recorded in the metafile's own header, then rasterized and analyzed like any other embedded image.
+    *   Validation is strict — record type, signature position, and a length that fits the remaining bytes — so a coincidental byte sequence is not mistaken for an image. Duplicate images are still collapsed and the per-document cap still applies.
+    *   (Ref: #1277, `functions_office_media.py`, `functions_documents.py`, legacy Office image extraction)
+
 ### **(v0.250.223)**
 
 #### Bug Fixes