Transpic
Translate text inside photos and graphics with AI while keeping the original layout.
Design Tools·AI & Machine Learning·E-Commerce
What is Transpic?
Transpic is an online AI image translator that translates the text inside a photo or graphic into another language while keeping the original visual layout. Users upload a JPG, PNG, or static WebP file such as a photo, screenshot, manga page, product graphic, poster, or document scan, choose from 60+ target languages, and download the translated result. A guest trial allows one image up to 3 MB without sign-up.
The tool is aimed at people who need to understand or localize visual content, such as e-commerce sellers adapting product graphics for new markets, manga readers, travelers reading menus or signs, and anyone translating document scans. It places each translated text block where the original text appeared, so a menu price stays beside the correct dish and a product benefit stays near its icon. Users can compare the source and translated image side by side, inspect individual text blocks, and make edits in an editor before exporting at the uploaded dimensions.
What sets it apart is the focus on layout-aware, editable output and private handling. Uploads are validated and stored privately rather than published as permanent public images. Batch upload is available on paid plans, and plan limits govern batch size, language count, watermarks, history, and commercial rights. Direct PDF and Word uploads are not supported, and images with tiny, curved, or heavily textured lettering may need manual adjustments. Users should verify names, prices, and numbers against the original before publishing.
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FounderPlaybooks.
What other founders did to grow.
2722 dispatches from hundreds of founders, pulled from the week's best podcasts.
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Subway Surfers spends nothing on paid ads — 10 people produce 5-10 assets a day and the flywheel runs itself
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Sean Ellis developed a simple test: if you were no longer able to use this product today, how disappointed would you be — very disappointed, somewhat, or not? The golden benchmark is 40%. Like Superhuman did, you can segment that data to find the nucleus of your highest-intent users and use it to build a product roadmap that moves you over the line.
Use the 40% disappointment test to quantify product-market fit — not just 'feel it when you see it'
The binary 'you'll know it when you see it' PMF definition is not actionable. The 40% threshold gives a directional score that can be tracked over time and segmented by user type. Superhuman ran this test iteratively — found they were below 40%, identified which segments were closest, talked to those users, built toward their needs, and systematically crossed the threshold.
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