AI image generators can produce a striking poster in seconds. The harder question is whether the result survives contact with a real campaign brief: a specific headline, a price, a date, a product name, and a layout that must work in more than one language.
That is where casual visual judgment breaks down. A poster may look polished at first glance while containing a substituted character, inconsistent punctuation, a distorted logo, or text that becomes unreadable after a mobile crop. To compare models fairly, teams need a repeatable test rather than a gallery of favorite outputs.
This article presents a compact rubric for evaluating multilingual poster generation. It is designed for practical selection work, not for declaring one permanent winner. Models and interfaces change, so the goal is to make each decision inspectable and reproducible.
1. Freeze one production brief
Start with a brief that resembles an asset your team might actually publish. Avoid a vague request such as “make a beautiful poster.” Define the content and constraints before opening any generator.
A useful test brief contains:
- one six-to-eight-word headline;
- one product or event name that must remain exact;
- a date, price, or percentage;
- a short call to action;
- a required visual hierarchy;
- one reference image for the product or character;
- a target aspect ratio and final display size.
Create localized versions in at least two writing systems. For example, pair English with Simplified Chinese, Japanese, Arabic, or Devanagari. Keep the meaning and information hierarchy equivalent even when line length changes.
The frozen brief matters because changing the wording for each model changes the test. Save the exact prompt, reference files, settings, and generation time alongside every output.
2. Score first-pass text accuracy
Judge the first output before repairing it. First-pass performance shows how much manual recovery a normal user should expect.
For every required text element, score four dimensions from 0 to 2:
- Character accuracy: 0 for unusable text, 1 for a small error, 2 for an exact match.
- Completeness: 0 when content is missing, 1 when partially present, 2 when every required element appears.
- Legibility: 0 when unreadable, 1 when readable only at full size, 2 when clear at the target display size.
- Hierarchy: 0 when the intended order is lost, 1 when partially preserved, 2 when headline, detail, and call to action are clearly differentiated.
Do not award points merely because a line resembles writing. Compare it character by character with the approved copy. Numbers, currency symbols, punctuation, diacritics, and full-width characters deserve the same scrutiny as letters.
Record the score before selecting a favorite. Otherwise, the strongest composition can quietly bias the reviewer into overlooking text defects.
3. Test script-specific failure modes
Different scripts reveal different weaknesses. The rubric should include checks that match the language rather than treating all text as Latin characters with a different font.
For Chinese and Japanese, inspect component structure, simplified-versus-traditional substitutions, accidental character fusion, and line breaks that separate a phrase unnaturally. For Arabic, check joining behavior, reading direction, punctuation placement, and whether glyphs change incorrectly in context. For Devanagari, inspect conjuncts, vowel signs, and marks positioned above or below the correct character.
Also check mixed-script content. Product posters often combine a Latin brand name with local-language copy, numerals, and symbols. A model may render each script acceptably in isolation but lose spacing or hierarchy when they share a layout.
If nobody on the review team reads the language, ask a fluent reviewer to validate it. Optical character recognition can help flag differences, but it should not be the only judge of linguistic correctness.
4. Measure reference fidelity separately
Text accuracy and image consistency are distinct problems. Score the supplied reference independently so that an attractive approximation does not hide unwanted product changes.
Check the silhouette, key colors, material, label placement, distinctive details, and proportions. For a person or character, check identity cues, clothing, accessories, and relative scale. For a packaged product, pay special attention to the boundary between generated campaign text and the label already present on the reference.
Use a simple 0-to-2 score for each required attribute. A result can then be strong in typography but weak in product fidelity, or the reverse. That is more useful than reducing everything to one subjective “looks good” rating.
5. Run one controlled correction
After scoring the first pass, allow one repair instruction. Keep the correction narrow: “Replace only the headline with the exact supplied Chinese text; preserve the product, lighting, layout, and all other elements.”
Compare the corrected result with the first output. Note whether the target problem improved and whether unrelated regions changed. A model that fixes one line but redesigns the product, face, or background creates hidden production work.
Track three recovery metrics:
- number of correction attempts;
- time to an approvable result;
- amount of collateral change outside the requested region.
This step often changes the decision. A model with a slightly weaker first pass may be the better production choice if it follows precise edits without disturbing approved content.
6. Test the delivery crop, not only the canvas
Export the candidate at its real destination size. Review it as a social thumbnail, mobile card, marketplace tile, or printed proof—not only inside the generation interface.
Check that essential text remains readable, safe margins survive platform cropping, the call to action is not clipped, and the product retains enough visual area. Test both high-density and ordinary displays when the asset will appear on the web.
A helpful rule is to mark every required element as “must survive,” “may move,” or “decorative.” This makes crop decisions explicit and prevents reviewers from protecting background decoration while sacrificing campaign information.
7. Keep an evidence table
For each model, preserve the first output, corrected output, exact prompts, settings, generation time, and raw scores. Add a brief reviewer note describing the most important failure in plain language.
The final comparison should show:
- text accuracy by language;
- reference fidelity;
- layout and crop survival;
- correction success;
- total attempts and time;
- estimated workflow cost.
Publish failures as well as successes when sharing the test internally. A single misspelled price or drifting product label teaches more about production risk than ten unrelated showcase images.
A compact decision rule
Before testing, define the threshold for the intended job. A campaign poster might require exact headline, date, price, and brand name; a concept mood board may tolerate imperfect incidental text. The same output can be acceptable for one job and unusable for another.
Choose the model that clears the required threshold with the lowest recovery cost—not necessarily the model that produces the most dramatic first image. This keeps selection tied to the work the team must deliver.
For a current example of a text-focused generation workflow, the Nano Banana Pro page on PixMind provides a useful place to run this rubric with multilingual copy, reference images, and natural-language edits. The framework also works with any other image generator that supports the same tasks.
The larger lesson is simple: multilingual poster quality is measurable when the brief, inputs, scores, and correction budget stay fixed. A small repeatable test gives design teams evidence they can revisit after a model update, instead of relying on memory or a polished demo.
Disclosure: I work with PixMind on AI image workflow and content evaluation. The rubric above is platform-independent, and the relationship is stated so readers can assess the example transparently.
Originally published on Medium.
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