Choosing the 'Right' AI Image Generator

Gobbledygook Alert!

Why on earth are AI image generatora so rubbish at writing text? You can get them to whip up a stunning landscape or a ridiculously lifelike face, but ask for a simple logo with some legible words, and it all goes a bit pear-shaped. Letters get jumbled, spellings go wild, and your carefully crafted message turns into visual soup. It's a real head-scratcher, isn't it?

Why AI Struggles with Text in Images

The core of the problem lies in how these AI models learn. They're basically trained to see the world as a jumble of pixels, not as a collection of individual letters or symbols. Imagine it like this: the AI looks at millions of pictures where text pops up on signs, product packaging, or even someone's T-shirt. It learns that these squiggly bits often appear in certain places and have a particular texture, but it doesn't understand them as distinct characters that form words. So, for a landscape, a bit of blurriness or distortion is fine, maybe even artistic. But for text, that same fuzziness instantly makes an 'R' look like a blob, or worse, changes the meaning entirely.

Think about the training data too. A lot of text in real-world images is low-resolution, deliberately stylised, or partly obscured. So, the models end up learning to create "text-like textures" rather than precise, correctly spelled words. Plus, these models are usually judged on how visually plausible the whole image is. If it looks like a poster at first glance, the AI's done its job, even if the actual text is complete gibberish. It's a bit like when AI research gets choked by low-quality "AI slop", the input really impacts the output.

Why This Is a Big Deal for Real-World Uses

While it's a bit of a laugh when you're just messing about, this limitation can be a real headache for serious applications. If you're in marketing, design, or creating packaging, you need exact taglines, legible product labels, and compliance copy that's absolutely spot on. There's no room for error. This problem gets even bigger when you're dealing with languages that have complex scripts, like Chinese, Japanese, or Thai. Their characters are visually dense, and there's often less high-quality, labelled training data available compared to English.

"For casual experimentation, this limitation is just a quirk. For serious use cases in Asia and beyond, it can be a deal-breaker."

Because of this, many creative teams use AI for the initial layout and mood, but then they have to switch back to traditional tools for the actual text. So, you might generate a cool graphic with AI, get the composition just right, and then pop it into something like Figma or Photoshop to manually add the correct copy. It's a hybrid approach that lets AI handle the heavy lifting of ideation, while humans ensure accuracy and brand consistency. It's a bit like the idea of Human-AI Skill Fusion, where the best of both worlds come together.

The Models That Are Getting Better at Text

Despite the general struggle, some newer systems are actually quite a bit better at rendering readable text. They're using clever training strategies, better ways to handle text conditioning, and sometimes even specific objectives tailored for typography.

• Ideogram (v3 and newer) has really made a name for itself with posters, logos, and general designs that feature clear, correctly spelled text, especially for shorter phrases. Many designers swear by it for stylised yet legible lettering.
• DALL·E 3 (available through ChatGPT) is one of the most reliable general-purpose models, particularly for multi-line, instruction-driven English text. You can give it pretty detailed prompts about wording, line breaks, and layout, which is handy for things like book covers or ad creatives. It shows how AI chatbot giants are restricting free access to these powerful tools.
• Emu 3.5 and other newer Meta models have shown impressive progress with short, high-impact text, like badges or buttons. This is great for UI-style graphics or simple titles. Benchmarks even show higher exact-match rates for short strings compared to older diffusion models. You might recall when Meta AI offered free Midjourney & Flux.
• Adobe Firefly (integrated into Photoshop) is built with text effects and creative typography in mind. It's a solid choice if you want stylised lettering and you're already deep in an Adobe workflow.

For businesses, especially those in regions like Asia, the choice of tool also depends heavily on the language. Many leading systems are strongest in English, and while some models can handle simple signs in major Asian languages reasonably well, the consistency and spelling often lag behind.

Practical Tips for Getting Better Text from AI Today

Even with the best models, how you prompt them and your workflow can make a huge difference. Here are a few simple tricks to get more usable text:

• Keep it short and sweet: Titles, labels, and logos work far better than paragraphs.
• Centralise the text: Place it in a clear, central area so the model "pays attention" to it.
• Be super explicit in your prompts: Tell the AI exactly what you want, like "exactly this text, spelled correctly, in all caps, with line breaks like this." Avoid cramming too many unrelated instructions into one request.
• Treat AI as a layout and style engine, not a typesetter: Generate a few options, pick the best one, and then manually replace the AI-generated text with your precise copy using your preferred design software. This extra step might add a few minutes, but it saves a lot of headaches!

What's Next for AI and Text

The good news is that text rendering in AI is improving really quickly. Developers are experimenting with fancy new ways to combine pixel-level image generation with specific character-level controls, better multilingual training sets, and post-processing steps to clean up text. As these techniques mature, we can definitely expect AI tools that handle typography and multilingual layouts with far fewer mistakes. It's a bit like the continuous improvements we see in models like Gemini 3, which aims to be your everyday AI assistant.

Until then, if you're using AI image generation for anything serious, especially if you're working across diverse markets, it's wise to assume that text is still a bit of a weak spot. Pick tools known for better typography and, crucially, keep humans in the loop for those final, all-important words. This hybrid approach ensures you get the creative benefits of AI without sacrificing accuracy. You can read more about the technical challenges and advancements in text-to-image models in this academic paper from arXiv: Text-to-Image Generation with Text-to-Text Guidance.