AI AI Tools Guidebook
Troubleshooting

AI Image Text is Unreadable / Misspelled

Models still struggle with text-in-image. Two strategies: short words or post-edit.

Published: Author: AI Productivity Guide Team 🌐 查看中文版本

You generate a poster, a t-shirt design, a sign in a scene, or a logo concept, and the text comes back as nonsense: OPEM, RESPCT, DESEPTION, garbled letters that look like the right shape but are not actually words. Or worse: middle letters get fused, kerning is wrong, fonts shift mid-word. Despite improvements in 2025-2026 (Ideogram 2.0, Imagen 3, Nano Banana, Flux 1.1 Pro Ultra), AI-generated text remains one of the weakest generation tasks. The fix is almost always either a model swap or a post-process approach.

Common causes

Ordered by what most often produces unreadable output.

1. Model tokenization splits the text

Most image models tokenize text the same way LLMs do — by subword units, not characters. The word RESPECT may be tokenized as RES+PECT, but the model only has visual training data for “letter shapes,” not these specific subword chunks. It guesses at how the chunks should look, and gets the middle letters wrong.

How to spot it: Compare your intended word to the output. Are the first and last letters usually correct, with middle letters mangled? That is the tokenization symptom.

2. Long phrases drift across characters

The longer the text string, the more letters drift. A 4-letter word almost always works; a 20-character phrase almost always has errors. The accumulation is exponential.

How to spot it: Count letters in your text. Under 8 letters and a strong model should handle it; over 15 letters and you need a different strategy.

3. Stylized fonts confuse the model

“Gothic blackletter,” “graffiti tag,” “neon script,” “handwritten cursive” — these stylization instructions push the model into low-data territory where letter shapes are more varied in training, and the model is less confident about specific letter glyphs.

4. Multiple text elements in one image

A storefront sign + a chalkboard menu + a price tag — each text element fights for the model’s text-rendering capacity. Quality drops on all of them.

5. Text in unusual placements

Text wrapped around a curved object, text in deep perspective, text reflected in a window or mirror — these all require the model to deform letter shapes coherently, which it rarely does.

6. Wrong model for text-in-image

Older SDXL checkpoints, anime-focused models, and most stylized checkpoints are very weak at text. Even Midjourney v6 struggled significantly. The strongest text-in-image models as of late 2025 are: Ideogram 2.0, Imagen 3, Nano Banana, Flux 1.1 Pro Ultra, and DALL-E 3.

7. Text in a script you do not also write English in

Chinese, Japanese, Arabic, Cyrillic, etc. The model has less training data and less reliable character rendering. Even strong text models often fail on non-Latin scripts.

Before you change anything

  • Save the prompt, model, and the broken-text output.
  • Decide: is the exact text critical (logo, legal, brand) or is the visual concept enough (background sign, atmospheric detail)?
  • If exact text is critical, plan for either a strong text-in-image model or a post-edit pass.
  • Confirm whether the text needs to match a specific brand font; if yes, AI generation is the wrong tool for the final asset.
  • Commit or back up the current prompt template before changing it.

Information to collect

  • Full prompt and the intended text string.
  • The model and tier used to generate the broken output.
  • Examples of the broken text outputs (3-4 attempts so you can see the failure pattern).
  • The intended use case (poster, logo concept, scene detail).

Shortest path to fix

Step 1: Cut the text to 5 characters or fewer

The single biggest reliability gain. Short single-word text works far more reliably than phrases:

  • OPEN instead of OPEN FOR BUSINESS
  • 2026 instead of LIMITED EDITION 2026
  • SALE instead of BIG SUMMER SALE

If the use case allows it, ship the short version.

Step 2: Switch to a text-strong model

In rough order of text rendering quality (2025-2026):

  1. Ideogram 2.0: purpose-built for text-in-image. Use this first if text matters.
  2. Imagen 3 (Google) — very strong on English text.
  3. Nano Banana (Google’s recent image model) — strong typography.
  4. Flux 1.1 Pro Ultra: improved text rendering over Flux Dev.
  5. DALL-E 3 (via ChatGPT) — solid for short English phrases.

For non-Latin scripts: results are weaker across the board; test each model on your specific script.

Step 3: Use quoted explicit text in the prompt

Most strong text-in-image models look for quoted text:

A vintage neon sign that reads "OPEN", glowing red on a brick wall

Without quotes, the model is more likely to interpret “open” as a concept rather than a literal text rendering.

Step 4: Generate background separately, type text yourself

The most reliable workflow for any use case where exact text matters:

  1. Generate the image without the text (or with placeholder text).
  2. Open in Figma, Canva, Photoshop, or Affinity.
  3. Set type with a real font.
  4. Place it where the AI text would have been.

This takes 60 seconds and is 100% reliable.

Step 5: For logo / brand text, never generate

A real logo requires vector output, exact kerning, and brand color compliance. AI raster output cannot satisfy any of these requirements. Use AI for logo concepts only; commission or design the final logo in Figma / Illustrator.

Step 6: For stylized text, try image-to-image from real text

  1. Type the exact text you want in Photoshop / Figma using a normal font.
  2. Export at 1024x1024.
  3. Use it as image-to-image input with denoise 0.3-0.4 and a stylization prompt: graffiti spray-paint style, neon glow on brick wall.

This anchors letter shapes to a known-good text image and stylizes around them. Works far better than pure text-to-image for stylized text.

Step 7: For unfixable cases, inpaint the text region only

Generate the rest of the image first. Mask the text region in Photoshop / SDXL inpaint. Provide the exact text as a separate prompt. Repeated inpaint passes can usually nail short text strings.

How to confirm the fix

  • The text reads cleanly without garbled middle letters.
  • A second viewer can read the text without needing context.
  • All instances of multi-word text are spelled correctly and consistently.
  • The font style matches your intent (script, serif, etc.).

If it still fails

  1. Drop to the shortest possible text (one word, ideally 3-4 letters).
  2. Switch to the most text-strong model on your list (Ideogram 2.0 first).
  3. Generate background separately, place text in post — this is the 100% reliable path for any production use case.
  4. For non-Latin scripts, accept that AI generation is currently unreliable; post-edit.
  5. Package the prompt, model, intended text, and the broken output before asking community help.

Prevention

  • Default text-in-image to “post-edit the text” rather than “ask the model for it.”
  • Standardize on a text-strong model (Ideogram 2.0) for any project where text matters.
  • Don’t rely on AI for legal, branded, or trademark-sensitive text — generate without it and add in post.
  • Build a “text-in-image workflow” doc with the model choice, prompt structure, and post-edit step for your specific projects.
  • For non-Latin scripts, plan post-editing into every project from the start.

Tags: #Prompt #Debug #Troubleshooting #Image generation

Related Articles