How I Built an AI Tool to Remove Text, Watermarks, and Unwanted Objects from Images

A lot of image editing tasks are surprisingly repetitive.

Not full Photoshop-level editing — just the annoying everyday cleanup work:

• removing text from a screenshot
• deleting a watermark from a mockup
• erasing a distracting object from a photo
• cleaning up an image before reusing it in a document, ad, or design
• removing a background from a product image

I kept running into these cases, so I built a small web tool for it:

ImageCleanupAI

It’s an online AI image cleanup tool that helps users remove text, watermarks, unwanted objects, and backgrounds from images directly in the browser.

The problem I wanted to solve

Many image tools are either:

1. too heavy for simple cleanup tasks
2. too limited to produce natural-looking results
3. too “one-click” with no way to refine the output

In practice, image cleanup usually needs both automation and control.

For example, AI may correctly detect most of a text overlay, but the user still wants to refine the mask around edges, shadows, or textured backgrounds. A one-click tool alone often isn’t enough.

That was the main idea behind this project:

Use AI to accelerate the cleanup, but still let the user adjust the result manually.

What the tool does

ImageCleanupAI currently focuses on a few practical workflows:

• Remove text from image
• Remove watermarks
• Remove unwanted objects
• Remove image backgrounds

The basic flow is simple:

1. Upload an image or screenshot
2. Let AI detect the removable region
3. Refine the mask manually if needed
4. Export the cleaned result

The goal is not to replace advanced image editors.

The goal is to make common cleanup work much faster.

Why I focused on text removal first

Text removal is one of the most common and underestimated image-editing needs.

Examples:

• screenshots with labels or UI text
• social media images with overlaid captions
• memes or thumbnails with large text blocks
• scanned images with extra marks
• product or promo visuals that need reuse in another context

In many of these cases, the background behind the text is the hard part.

Removing the text itself is easy.

Reconstructing the surrounding pixels so the result looks natural is the real challenge.

That’s where AI-based cleanup and inpainting become useful.

What makes this type of tool useful

From a product perspective, I think tools like this are valuable when they do three things well:

1. Reduce friction

Users should be able to upload an image and start editing immediately in the browser.

2. Handle real-world messiness

Text is rarely placed on a flat white background. It sits on gradients, photos, textures, shadows, and noisy screenshots.

3. Allow refinement

Automatic detection is great, but manual adjustment is what makes the final output usable.

That’s why I prefer the combination of:

• AI auto detection
• brush/eraser refinement
• before/after preview
• fast export

Typical use cases

Some common scenarios this tool is built for:

• cleaning up screenshots for blogs or documentation
• removing text from images before translation or redesign
• deleting small objects from product or lifestyle photos
• preparing images for e-commerce listings
• removing watermarks or overlay elements from design drafts
• generating cleaner assets for social posts and presentations

Building small AI products around narrow tasks

One thing I’ve been thinking about a lot is that AI products do not always need to be broad.

Sometimes the better product strategy is:

• pick one narrow workflow
• make it significantly faster
• reduce decision fatigue
• keep the UI simple
• ship quickly and iterate based on actual usage

“Image cleanup” is a good example of that.

It is not a glamorous category, but it is a very real one.

People need it all the time.

And because the workflow is concrete, it’s easier to improve through product iteration:
better detection, cleaner masks, more natural reconstruction, faster exports, better edge handling, better support for screenshots, and so on.

Current focus

Right now I’m continuing to improve things like:

• natural-looking cleanup on complex backgrounds
• mask refinement experience
• export speed
• separate landing pages for specific use cases
• broader support for real-world image cleanup scenarios

The project

If you want to try it, the site is here:

https://imagecleanupai.com/

I’d be especially interested in feedback from people who work with:

• content creation
• design
• e-commerce
• screenshots/documentation
• image-heavy SaaS workflows

I’m also curious how others here think about building narrow AI utilities:
do you prefer a focused single-purpose tool, or a larger all-in-one editor?

Thanks for reading.