Edit Labels in AI-Generated Figures

Why AI-generated labels are hard to fix

Most AI image generators export scientific figures as PNG or JPG. The label may look like text, but it is usually just pixels inside one flat image. That means a single wrong unit, axis note, pathway name, or callout can force a manual redraw.

Editable Figure gives you two practical paths: quick visual label replacement for small edits, or structured SVG conversion when the figure needs object-level control.

Choose the right label-editing workflow

Start with the smallest workflow that gives you enough control.

• Use Basic Canvas when you only need to cover and replace a few labels on a simple background
• Use AI Vector Canvas when labels should become part of an editable SVG diagram workflow
• Use SVG export when arrows, boxes, callouts, legends, colors, and spacing need to change together
• Use partial image embed SVG when detailed visual regions should remain stable while surrounding labels stay easier to revise

Common AI figure label problems

Generated figures often need cleanup before they are safe for slides, posters, or manuscript drafts. Look for misspelled gene names, incorrect abbreviations, inconsistent capitalization, fake words, repeated labels, broken legends, and units that do not match the data.

If labels are tied to arrows, icons, boxes, or grouped panels, converting the figure to SVG first can make the full cleanup easier than patching each word on a flat image.

Check labels like scientific content, not decoration

Before exporting, read every label as part of the figure's argument. Check terms, numbers, panel order, arrow direction, legend mapping, abbreviation consistency, and whether labels are close enough to the elements they describe.

For publication-oriented cleanup, keep an editable source file so late terminology changes do not require recreating the entire figure.