Global Scale Picture Text Translation

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About This Site

AI tool that translates text within images while perfectly preserving original layout. AI Image Translator is an advanced online artificial intelligence tool designed to translate text within images into a target language while keeping the original layout perfectly intact. Moving beyond simple text recognition (OCR), it utilizes cutting-edge AI and a leading neural translation engine to erase original text, intelligently reconstruct backgrounds, and match original font styles and colors. It supports various image formats including JPG, PNG, WebP, BMP, GIF, and PDF, and can handle text in more than 100 languages. It provides efficient and professional solutions for localizing screenshots, comics, product images, and everyday visual content, and features batch processing to translate multiple files at once.

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Key Features AI

Core Features
Layout Preservation: Ensures translated text matches the original font, style, spacing, and color.
Seamless Inpainting: Intelligently reconstructs the background after erasing original text for a clean output.
Batch Processing: Allows users to run efficient batch operations across entire catalogs or multiple files at once.
Multi-Language Fusion: Unifies visuals containing multiple source languages into a single, clear translation.
Fast Processing: Typically completes a standard translation task in about 30 seconds.
Advantages
High visual fidelity by matching original fonts and layout structure.
Supports over 100 languages, including less common languages.
Fast translation speeds, usually processing in under a minute.
Strict no-trace privacy policy with encrypted file transit and secure deletion.
No login required for basic free usage up to 5 translations per month.
缺点:Free tier has limits on usage (5 free translations per month) and file size (up to 5MB).
缺点:Images with dense text or highly complex backgrounds may extend processing time up to 1 minute.
缺点:The highest accuracy model (Aimslate) may compromise layout preservation quality compared to the layout-priority model.

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08-22 09-05 09-20

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