How Image Translation Actually Works: From Pixels to Translated Text

Translating text on a web page is easy. The browser already knows where the text is, what each sentence says, and how it should be displayed. Images are different. A screenshot, restaurant menu, product label, poster, or scanned document is just a collection of pixels. Before we can translate anything, we first need to figure out where the text is, recognize what it says, translate it, and somehow put the translated text back into the image. That makes image translation an interesting combination of computer vision, OCR, machine translation, and image generation. Step 1: Finding Text Inside the Image The first problem is text detection. An image may contain multiple text blocks with different fonts, sizes, colors, and orientations. Text can also appear on complex backgrounds, signs, packaging, screenshots, or photographs. A typical OCR pipeline first detects regions that are likely to contain text. Instead of seeing: Welcome to Tokyo Platform 3 Exit A the system initially see