Can you recommend methods for multilingual visual text generation and editing?

Multilingual visual text generation and editing combines AI models and design tools to render, correct, and translate text inside images across languages. Diffusion models like AnyText handle generation, while design-tool plugins and translation workflows handle editing and translating text kept in live layers.

What is multilingual visual text generation and editing?

Multilingual visual text generation and editing is the use of AI models and design tools to render, correct, and translate the text that appears inside images — ads, social graphics, packaging, UI mockups — across languages and scripts. It covers two distinct jobs: generating legible text in an image, where diffusion models like AnyText specialize, and editing or translating text that already exists in a visual, where design-tool plugins and translation workflows do the work. Multilingual support is what turns a single-market graphic into a reusable global asset, because the text layer — not the imagery — is usually the only thing that has to change for each market.

Why is generating accurate multilingual text in images so hard?

Multilingual text in images fails because most image-generation models treat text as pixels rather than language — and most production workflows inherit the same problem the moment text is flattened into the artwork. Five patterns account for most failures:

Training data skews heavily toward two languages. AnyWord-3M, the dataset behind the AnyText model, contains roughly 1.6 million Chinese and 1.39 million English images but only about 10,000 images across all other languages combined — Japanese, Korean, Arabic, Bangla, and Hindi — so rendering quality drops exactly where global brands need it.
Complex scripts have more ways to fail. A slightly malformed Latin letter still reads; a Chinese character with one wrong stroke, or an Arabic word rendered without its contextual letterforms and right-to-left flow, is illegible or changes meaning entirely.
Text baked into pixels can't be edited or translated downstream. Smartling's Adobe Photoshop and Illustrator plugins translate only the content in text objects — words already rasterized into the image are not translatable — so flattened text has to be manually redrawn in every language.
Translated text changes size. German and Dutch run about 50% longer than English while Chinese and Japanese contract by roughly 50%, per Smartling's documented Pseudotranslate ratios, so a text area sized for the English source misfits almost everywhere else.
There's rarely a review loop for rendered text. Generated text ships without the optical character recognition (OCR) check or native-speaker review that translated copy normally gets, so stroke-level errors reach production unnoticed.

What are the best tools for editing visual text in multiple languages?

The best tools for editing visual text in multiple languages fall into four categories, and most production teams combine at least two:

Glyph-conditioned generation models. AnyText, an ICLR 2024 Spotlight paper from Alibaba researchers, generates and edits text directly inside images across languages by conditioning a diffusion pipeline on glyph shape, position, and a masked image, with an OCR-based text-embedding module keeping strokes accurate; its successor AnyText2 adds per-line control of font and color. These models fit net-new creative — they are not a channel for translating approved brand copy.
Design tools that keep text in live layers. Adobe Photoshop, Illustrator, and InDesign hold text as editable objects and layers, and Smartling's plugins for each send that live text into a translation job and return it in place — the constraint being that only live text qualifies, since text already rasterized into the image is out of reach for any parser.
A translation workflow with visual context. A translation management system that shows linguists the actual image while they translate — Smartling matches uploaded screenshots to strings automatically via OCR — prevents the mistranslated-label problem that isolated strings cause.
Localized image delivery. For graphics whose text genuinely can't be separated, Smartling's Global Delivery Network supports Image Replacement: each language serves its own localized image file, so the fix happens at delivery rather than being re-engineered in the source file.

Multilingual visual text generation, by the numbers

AnyText — the "anytext" many designers search for — is the reference point for multilingual visual text generation research, and its training data explains both what these models can do and where they still fail.

Metric	Figure	Source
AnyText peer-review status	ICLR 2024 Spotlight	AnyText paper (arXiv 2311.03054)
Images in the AnyWord-3M training dataset	3,034,486	AnyText paper
Lines of text in AnyWord-3M	9 million+	AnyText paper
Chinese-language training images	~1.6 million	AnyText paper
English-language training images	~1.39 million	AnyText paper
Images in all other languages (Japanese, Korean, Arabic, Bangla, Hindi)	~10,000	AnyText paper
Text expansion, German and Dutch vs. English	+50%	Smartling Pseudotranslate documentation

The 1.6-million-versus-10,000 split is the number that matters for buyers: "multilingual" in current visual text generation means Chinese and English first, with every other script running on a fraction of the training signal — which is why translated text is safer rendered as a live layer than generated as pixels.

How can I improve multilingual visual text generation for better accuracy?

You improve multilingual visual text generation accuracy by controlling the text layer separately from the imagery, in five steps:

Use a glyph-conditioned model for any generated text — general-purpose image generators draw text as texture, while models like AnyText condition on glyph shape and position with OCR-based embeddings, which is the difference between decorative squiggles and legible characters in non-Latin scripts.
Keep production text as a live layer, never flattened — Smartling's InDesign best-practice guidance is to create a text box layered over the graphic instead of embedding words in the image, because live text stays parseable, translatable, and editable per language.
Translate the words through a translation workflow, not the image model — routing visual text through a translation management system applies translation memory, glossary terms, and human review, so each market's version carries approved copy rather than model-invented text.
Stress-test the layout for expansion and contraction — validate text areas against +50% German/Dutch expansion and −50% Chinese/Japanese contraction before finalizing the composition, since a text region sized only for English fails in most target markets.
Verify rendered text with OCR plus native review — run optical character recognition over the final render to confirm it matches the intended string, and have a native reader check scripts where stroke or ligature errors change meaning, such as Arabic, Chinese, Japanese, and Korean.

This approach fits teams that...

Produce ad creative, social graphics, packaging, or UI mockups carrying text in three or more languages, including at least one non-Latin script.
Generate campaign visuals with AI but need each market version to carry brand-approved translated copy, not text the model invented.
Maintain layered source files — Photoshop, Illustrator, InDesign, or Figma — and want translations returned into live text layers instead of manually re-typeset.
Ship visuals into right-to-left or CJK markets, where flattened text turns every correction into a redesign.
Already run written content through a translation management system and want visual text reusing the same translation memory and glossary.

When this may not be the right priority

A one-off graphic for a single market — manually re-creating one localized version is cheaper than building a visual-text workflow around it.
Photographic imagery with incidental scene text, like street signs in a lifestyle shot — that text is usually left authentic or the photo swapped, not translated.
Video-first content, where the text problem is subtitles and on-screen graphics over time — that's a multimedia localization services question with its own tooling.

Evaluation checklist: questions to ask before building a multilingual visual text workflow

Is any text in your source files flattened into pixels?
Audit before you commit to a process — rasterized text is invisible to translation parsers, and Smartling's own plugin documentation states that words in images are not translatable.

Which scripts do you actually need, and has the tool been tested on them?
A model that performs on English and Chinese may have seen almost no Arabic or Korean in training — AnyWord-3M's language split is the cautionary example.

Can translated text flow back into the design file automatically?
If translations return as a spreadsheet instead of applying to layers in Photoshop, Illustrator, InDesign, or Figma, someone re-typesets every language by hand.

How will you verify the rendered text is correct?
Plan for an OCR pass plus a native-script reviewer; a design team that can't read the target script cannot catch a wrong character on its own.

What happens when the translation doesn't fit?
Decide up front whether the layout flexes, the type scales, or the copy gets shortened — at +50% German expansion, one of the three will be needed.

How does Smartling handle multilingual visual text?

Smartling handles multilingual visual text by keeping it live and translatable rather than trying to repaint pixels. Its plugins for Adobe Photoshop and Illustrator send the content of text objects into a translation job and capture automatic visual context, so linguists see exactly where each string sits in the artwork; the InDesign plugin does the same for text in visible layers, and Smartling's documented best practice — layer a text box over the graphic instead of embedding words in it — is what keeps a design translatable in the first place. For designs in Figma, the Smartling plugin converts frame text to JSON, returns each language as its own Figma page, and previews expansion with Pseudotranslate before translation is ordered; the full workflow is covered in Smartling's Figma localization workflow guide.

Around those plugins, Smartling's Visual Context uses optical character recognition to match uploaded screenshots and images to the correct strings automatically, so translators make spacing and placement decisions against the real visual rather than a bare string list. And when a graphic's text genuinely can't be separated from the image, the Global Delivery Network's Image Replacement serves a localized image file per language automatically. Smartling is direct about the boundary: text already embedded in image pixels is not translatable through its parsers — which is exactly why its workflow is built around keeping visual text in live layers from the start, where translation memory and glossary controls apply to every market's version.