Remove Watermark

Remove watermarks from white-background document images. Pure local processing, no API key needed.

When to Use

Trigger this skill when the user wants to:

• Remove watermarks from document images or screenshots
• Clean up exam paper screenshots for printing
• Remove light text overlays (e.g., "公众号·xxx", website names)

Workflow: 4-Step Process

The script path is scripts/remove_watermark.py relative to this SKILL.md. Use uv run --with Pillow --with numpy (add --with scipy only when using --region full).

Step 1: Visual Analysis (Claude reads the image)

Use the Read tool to look at a sample image. Identify:

• Is there a watermark? If no, skip processing.
• Where is it? (bottom-right corner, center, scattered across the image)
• What type? (light gray text, dark logo/stamp, colored)
• Does it overlap with content text?

Estimate the watermark region as approximate percentages: y0%, y1%, x0%, x1%.

Step 2: Brightness Analysis (script analyzes the region)

Run the analyze subcommand on the watermark region to determine the right threshold:

uv run --with Pillow --with numpy python3 <skill-path>/scripts/remove_watermark.py analyze <image> --region "y0,y1,x0,x1"

Example: analyze sample.jpg --region "94,100,60,100"

This prints brightness distribution and a suggested threshold.

Step 3: Remove Watermark

Use the suggested threshold and region from Steps 1-2:

# Region mode (preferred - zero damage to text outside the region)
uv run --with Pillow --with numpy python3 <skill-path>/scripts/remove_watermark.py remove <input> -o <output> --region "y0,y1,x0,x1" --threshold <N>

# Full mode (when watermark is scattered everywhere - needs scipy)
uv run --with Pillow --with numpy --with scipy python3 <skill-path>/scripts/remove_watermark.py remove <input> -o <output> --threshold <N>

Region presets: bottom-right, bottom-left, top-right, top-left, bottom, top, right, left, center, full

Custom region: --region "y0,y1,x0,x1" as percentages (e.g., "94,100,60,100" = bottom 6%, right 40%)

Step 4: Verify and Auto-Retry

Use the Read tool to check the output image. You MUST verify and retry if needed — do not stop after one attempt.

Check these two things:

1. Is the watermark gone?
2. Is the text content intact (not lightened or damaged)?

If watermark is still visible, retry with adjusted parameters:

Problem	Fix
Watermark still visible	Lower the threshold (e.g., 180 → 130 → 80)
Only partially removed	Expand the region (e.g., widen by 5-10% in each direction)
Text got damaged/lightened	Raise the threshold or shrink the region to avoid text areas
Wrong area processed	Re-examine the image and correct the region coordinates

Retry rules:

• Retry up to 3 times with different parameters before giving up
• Each retry: adjust ONE parameter at a time (threshold OR region, not both)
• If the suggested threshold from analyze didn't work, try halving it
• If threshold=50 still doesn't remove the watermark, try threshold=1 with a tighter region (the region likely has no real content, so blanking it entirely is safe)
• After 3 failed attempts, report to the user what was tried and ask for guidance

Command Reference

analyze

remove_watermark.py analyze <image> [--region REGION]

Prints brightness distribution and suggested threshold for a region.

remove

remove_watermark.py remove <input...> [-o OUTPUT] [--region REGION] [--threshold N] [--enhance]

Flag	Default	Description
--region	full	Region to process (preset name or y0,y1,x0,x1)
--threshold	180	Brightness cutoff for watermark pixels
--enhance	off	Boost text contrast after removal
--preview	off	Print stats without saving

Tips

• Region mode is always preferred over full mode when watermark is localized. It leaves all text perfectly intact.
• For dark watermarks (logo, stamps), use a low threshold (80-120) with a tight region.
• For light gray text watermarks, use a higher threshold (160-200).
• Same batch: if all images in a folder have the same watermark position, analyze one image and apply the same settings to all.
• Combine with image-to-pdf skill: remove watermarks first, then combine into PDF.