docx-shrinker 0.1.3

Shrink and sanitize Word (.docx) documents by converting Visio embeddings, compressing images, and stripping metadata

docx-shrinker

Shrink and sanitize Word (.docx) documents. Converts embedded Visio diagrams to raster images, compresses oversized media, deduplicates files, and strips metadata, comments, tracked changes, macros, and other cruft.

What it does

1. Convert Visio embeddings — .vsdx → PDF (via Visio COM) → JPG/PNG (via PyMuPDF). Falls back to keeping the EMF preview when Visio is unavailable.
2. Convert OLE objects — Replaces legacy VML <w:object> blocks with modern DrawingML <w:drawing> inline pictures.
3. Compress images — Resizes raster images exceeding a pixel width threshold and re-compresses JPGs.
4. Deduplicate media — Identifies identical files by hash and rewrites relationships to point to a single copy.
5. Strip personal info — Removes author, last modified by, company, manager, keywords, and other document properties.
6. Remove comments and tracked changes — Deletes comment files and accepts all revisions inline.
7. Strip bookmarks — Removes auto-generated bookmarks (_GoBack, empty).
8. Remove garbage parts — Thumbnail, VBA macros, printer settings, ActiveX controls, custom XML data.
9. Clean up — Updates [Content_Types].xml and .rels files to reflect removed parts.
10. Validate output — Checks ZIP integrity and presence of [Content_Types].xml before finalizing.

Requirements

• Python 3.10+
• Microsoft Visio (optional) — required only for converting embedded .vsdx to high-quality images. Without it, Visio diagrams fall back to their low-resolution EMF previews.

Installation

pip install docx-shrinker

Or with uv:

uv tool install docx-shrinker

Usage

Command line

docx-shrinker report.docx

This produces report (shrunk).docx in the same directory.

Specify an output path:

docx-shrinker report.docx output.docx

Options

Flag	Default	Description
--format {jpg,png}	png	Image format for converted Visio figures
--dpi N	300	Effective rasterization DPI. Every figure renders at this DPI unless the result would exceed --max-megapixels.
--quality N	95	JPG quality (1–100). Ignored for PNG.
--max-megapixels N	100	Cap on output pixel count per image, in megapixels. Images exceeding the cap are downscaled, preserving aspect ratio. 0 to disable.
-i, --interactive	off	After conversion, show top 5 largest images and offer to re-convert at different quality
--version		Show version and exit

Examples

Convert Visio figures to JPG at 150 DPI:

docx-shrinker report.docx --format jpg --dpi 150

Aggressive compression (lower quality, tighter megapixel cap):

docx-shrinker report.docx --quality 80 --max-megapixels 25

Interactive mode to fine-tune large images:

docx-shrinker report.docx -i

Python API

from docx_shrinker import shrink_docx

result = shrink_docx("input.docx", "output.docx", fmt="png", dpi=300, quality=95)

print(f"{result['original_size_mb']} MB -> {result['new_size_mb']} MB")
print(f"Reduction: {result['reduction_percent']}%")

The result dict contains:

Key	Type	Description
original_size_mb	float	Original file size
new_size_mb	float	Output file size
reduction_mb	float	Size saved
reduction_percent	float	Percentage reduction
output_path	str	Path to the output file
visio_converted	list	(name, size_kb) tuples for each converted Visio diagram
visio_removed	int	Number of .vsdx embeddings removed
images_compressed	list	(filename, old_kb, new_kb) tuples
duplicates_removed	int	Number of duplicate media files removed
comments_removed	int	Number of comment files removed
bookmarks_removed	int	Number of bookmarks removed
garbage_removed	list	Names of removed garbage parts
warnings	list	Warning messages (e.g., Visio unavailable)

How it works

A .docx file is a ZIP archive containing XML and media files. docx-shrinker extracts the archive into a temp directory, applies all transformations in-place, then repacks it into a new ZIP. The original file is never modified.

Visio diagrams embedded as OLE objects include both the full .vsdx source and a low-resolution EMF preview image. docx-shrinker replaces these with a high-quality raster render and strips the heavy .vsdx originals — often the single biggest source of bloat.

Technical reference

Processing pipeline

shrink_docx() unpacks the .docx ZIP into a temp directory, applies all transformations in-place, then repacks into a new ZIP. The original file is never modified.

1. Visio conversion (.vsdx → PDF → image)

Embedded Visio diagrams are OLE objects containing both the full .vsdx source and a low-resolution EMF preview. The conversion pipeline:

1. _export_vsdx_to_pdf — Opens each .vsdx via Visio COM (ExportAsFixedFormat) and exports to PDF. This two-step path exists because Visio's direct raster export (Page.Export to PNG/BMP) produces extremely low-quality output. The PDF intermediate preserves full vector fidelity.

2. _restore_pdf_images — Visio's PDF export downscales and JPEG-compresses any raster images embedded in .vsdx files, even if the originals are lossless PNGs (e.g., a 1590x633 PNG becomes a 668x266 JPEG with visible chroma artifacts). There is no COM setting to control this. The fix: extract the original images from the .vsdx ZIP, match them to degraded PDF images by aspect ratio, and replace them with page.replace_image(). The corrected PDF is saved and reopened before rasterization. A critical detail: the PDF image transform matrix often has a negative Y scale (matrix.d < 0, since PDF origin is bottom-left), meaning the image data is stored vertically flipped. When replacing, the original must be flipped to match. Not all images need flipping — some transforms have positive Y scale.

3. _border_clip_rect — Visio always draws a 0.75pt black stroked rectangle at the page edges of every exported PDF. This border is not centered on the page boundary — left/top are nearly flush while bottom/right overshoot outward by 0.02–0.12pt. The code detects this rectangle via page.get_drawings() (typically drawing #0), computes per-side inset from the actual stroke overshoot, and clips the render rect inward to exclude both the stroke and its anti-alias fringe.

   Earlier approaches that failed:

   • Fixed uniform inset (0.5pt or 1pt) — either clipped content or left borders on some sides due to the asymmetric overshoot.
   • Pixel-level detection after rasterization — fragile; anti-aliased gray pixels don't pass a simple threshold, and results varied per image.
   • White rectangle overlay — the overlay's own edges get anti-aliased, replacing one faint border with another.

4. _render_pdf_to_image — Rasterizes the first page of the corrected PDF to PNG/JPG via PyMuPDF, using the computed clip rect. Renders at the requested effective DPI and downscales only if the result would exceed max_megapixels, so small and large Visio pages get the same quality.

   Alternatives that failed:

   • SVG embedding via <asvg:svgBlip> — The DrawingML spec supports embedding SVG alongside a raster fallback, and modern Word preserves SVG as vector through PDF export. But Visio COM's Page.Export("*.svg") produces SVG files that render as all-black shapes (stroke definitions survive, but fills, text styles, and images are lost or mis-referenced). There is no COM option to fix this. Since the SVG is unusable, the approach reduces to the raster pipeline anyway.
   • Post-hoc PDF figure replacement — Let Word export docx → PDF as usual, then replace the Visio-sourced raster images in the output PDF with vector content from the intermediate Visio PDFs. Matching the raster placements in Word's PDF back to their source figures is unreliable: Word re-encodes JPGs (defeating hash match), changes pixel dimensions (defeating dim match), and can reorder or reflow figures (defeating document-order match). Even when matching works, the "vector" Visio PDFs are mostly raster anyway because Visio's PDF export rasterizes complex shapes — see _restore_pdf_images for the partial recovery we already do.
   • Higher DPI is the practical answer. 300 DPI effective (the default) is sharp at normal reading zoom; 600 DPI is crisp up to 400% zoom. The megapixel cap (max_megapixels, default 100) keeps file sizes bounded when individual Visio pages are unusually large.

2. OLE/VML to DrawingML conversion

• extract_vml_dimensions — Parses width/height in EMU from <w:object> style attributes (handles both pt and in units).
• object_to_drawing — Rewrites legacy VML <w:object> blocks as modern DrawingML <w:drawing> inline pictures, preserving the image relationship ID and dimensions.
• next_doc_pr_id — Scans the document XML for the highest existing docPr/cNvPr id to generate unique IDs for new drawing elements.
• ensure_namespaces — Adds wp: and r: namespace declarations to the root <w:document> if missing, which is required for DrawingML elements.

3. Image compression

• compress_media_images — Re-encodes oversized raster images in word/media/. PNGs with high estimated compression potential are converted to JPEG. Existing JPEGs are re-saved at the target quality. Images whose pixel count exceeds max_megapixels are downscaled. Skips images that would grow larger after re-encoding.

4. Media deduplication

• dedup_media — Identifies identical files in word/media/ by MD5 hash. Keeps one canonical copy and rewrites all .rels references to point to it, removing the duplicates.

5. Metadata and markup stripping

• sanitize_core_props / sanitize_app_props — Strip personal info (author, last modified by, company, manager, keywords, etc.) from docProps/core.xml and docProps/app.xml.
• remove_comment_files — Deletes comments.xml, commentsExtended.xml, and commentsIds.xml.
• strip_comment_refs — Removes comment range/reference XML tags (commentRangeStart, commentRangeEnd, commentReference) from document XML.
• strip_revisions — Accepts all tracked changes inline: unwraps <w:ins> content, removes <w:del> blocks and their content, strips revision property tags (rPrChange, pPrChange, sectPrChange, tblPrChange).
• strip_bookmarks — Removes auto-generated bookmarks (_GoBack, empty-name).

6. Garbage part removal

• remove_garbage_parts — Deletes thumbnail, VBA macros (vbaProject.bin, vbaData.xml), printer settings, ActiveX controls, custom XML data, and the .vsdx embeddings themselves (after conversion).

7. Cleanup and validation

• clean_content_types — Removes [Content_Types].xml entries referencing deleted parts.
• clean_relationships — Removes .rels entries referencing deleted parts across all relationship files.
• _strip_xml_tags / _strip_nested_tag — Low-level helpers for removing XML tags, handling arbitrarily nested structures by processing innermost matches first.
• Output ZIP integrity is validated (well-formed ZIP, [Content_Types].xml present) before finalizing.

8. Interactive mode

• _interactive_reconvert — When -i is passed, presents the top 5 largest converted images and offers to re-convert selected ones at a different quality/DPI setting.

License

MIT