Google Targets Text Failures in AI Images: 9 things to know
Artificial intelligence assistants often produce convincing answers in chats, yet still struggle to place readable text inside generated images. Typos, warped letters and inconsistent fonts remain common issues, limiting how far designers can rely on these models for professional use.
A New Model Promises Sharper, Cleaner Text Google announced a new image generation and editing model called Nano Banana Pro. The company says it can render clear, accurate and well structured text inside images across multiple languages, solving a long standing weakness in AI visuals.
Gemini 3 Powers the Improvements The advancements are enabled by Gemini 3, Google’s latest large scale AI model launched earlier in the week. The company described the new version as a significant boost in reasoning and coding performance. The release was well received by investors and fuelled Alphabet’s record high share price.
Designed for Complex Graphics and Diagrams Google is positioning the model as a reliable tool for generating sophisticated graphics, including multi step diagrams, flow charts and layouts that require a mix of text and illustration. This is an area where many competing models still produce distorted output.
Part of Google’s Push to Monetise AI The announcement represents another attempt by Google to convert its AI research into commercial products. Free Gemini users worldwide will gain access to Nano Banana Pro with usage limits. Paid subscribers will receive higher quotas before being shifted back to older generations.
Integrations with Major Design Platforms Nano Banana Pro is now integrated with widely used design tools including Canva, Figma, Adobe Firefly and Photoshop. Google hopes this will encourage creative professionals to adopt the model within existing workflows.
Better Planning Before Rendering According to Google, the model can pre plan text placement, font attributes and alignment relative to other visual elements. By structuring the layout before the rendering stage, the system avoids the distortions that typically appear when models attempt to combine text and images on the fly.
Turning Text Heavy Tasks into Visuals The company says the model can convert the text of a recipe into an illustrated flow chart or produce a visual snapshot of real time information, such as weather updates or sports scores. These use cases highlight the growing interest in generative imagery for quick, informative design.
Support for Brand Assets and Creative Exploration For marketing teams, the model can accept up to fourteen reference images and recombine them in new scenes described in the prompt. This allows brands to experiment with creative concepts while maintaining the look and feel of their core visual identity.
Deep Control Over Photography Style Users can request specific camera angles, depth of field, colour grading or aspect ratios. The goal is to give creators enough precision to shape the final output as if they were directing a photo shoot.
