SELECTIVE AI TEXT REMOVAL
Upload an image, let OCR find its visible text, choose exactly which detected regions should disappear, and generate a clean PNG with the background rebuilt behind the selected words. The workflow removes existing lettering only; it does not translate the image or cover text with replacement boxes.
Upload one image. OCR detection and region selection appear after the image is ready.
Drop an image here
Or choose a JPG, PNG, or static WebP file from your device.
On a supported phone, Take a photo opens the rear camera so you can capture the menu or image directly.
Clean an owned product advertisement
The headline and feature claims are removed from the textured wall while the bottle, icons, leaf shadows, stone pedestal, lighting, and crop remain. Keeping selectable regions means an icon or product object does not have to disappear with nearby words.
REAL REMOVAL SCENARIOS
Drag each comparison to inspect the difference. These examples focus on three common, legitimate production tasks: preparing an owned product visual for new copy, recovering the clean photo behind a social caption, and turning an authorized storefront design into a reusable blank template.
The headline and feature claims are removed from the textured wall while the bottle, icons, leaf shadows, stone pedestal, lighting, and crop remain. Keeping selectable regions means an icon or product object does not have to disappear with nearby words.
Large lettering and a smaller subtitle are erased from the sky. The clean result continues the blue gradient and thin clouds instead of replacing the caption with a flat rectangle, leaving the coastline ready for a new campaign layout.
The painted shop name and two window-hour labels are removed as separate regions. Wood grain, glass reflections, bread displays, brickwork, warm lamps, and flower planters remain available for an authorized redesign.
Removal should be local and explicit. The service uses the OCR boxes you approve as edit instructions, expands its attention to visible letter edges, and reconstructs only those written regions. This is different from lowering opacity, hiding translated blocks, or painting a white patch over the words.
The goal is a clean plate, not a redesigned image. Products, people, scenery, icons, borders, lighting, colors, composition, and canvas dimensions should stay where they were. Complex fills can still require another generation, so always compare the result at full size.
HOW IT WORKS
The page separates detection from removal so you can inspect what the system found before committing an edit. That extra checkpoint matters when an image mixes editable captions with model numbers, prices, signs, or text that must remain.
Choose a JPG, PNG, or static WebP containing text you are authorized to edit. Clear exports and straight photos give OCR the strongest starting point.
OCR reads visible lines and returns normalized boxes over the source image. No background pixels are changed during this scan.
Check all text or only individual captions, labels, dates, or sign areas. Leave protected names, values, or interface labels unchecked.
AI removes the approved glyph regions and infers the nearby texture, gradient, reflection, or scene that should continue behind them.
Drag the comparison slider, inspect edges at full size, and download the private clean plate as a flattened PNG when it looks natural.
BACKGROUND RECONSTRUCTION
A text-free result is only useful when the fill matches its surroundings. Flat areas are usually straightforward, while gradients, patterned materials, reflections, faces, and fine geometry require more scrutiny. The examples and review flow make those differences visible rather than promising identical accuracy for every scene.
Solid walls, uncluttered labels, and broad matte surfaces offer strong surrounding pixels for reconstruction. Check for faint letter halos and make sure a backing plate was included with the selected region when necessary.
Smooth light changes expose obvious seams if the fill is too uniform. Compare both sides of the removed region and inspect whether cloud wisps, shadows, grain, and color transitions continue naturally.
Brick, foliage, glass, fabric, hair, and repeated product details are harder because letters cover meaningful texture. Select only the complete text area, then regenerate if the inferred structure introduces blur or repeated artifacts.
CONTROL BEFORE GENERATION
The core value is not a one-click claim; it is a transparent sequence that lets you see detected regions, decide what can be removed, and verify the generated clean image before downloading it.
Detected lines appear as boxes on the uploaded image and as readable checklist entries, making small captions easier to find on desktop and mobile.
Toggle boxes directly on the preview or use the checklist. The server validates every submitted block against the OCR attempt owned by the current user or guest.
Only selected OCR polygons are converted into a local mask. Torii receives the original image and that mask, so unchecked text and unrelated regions stay outside the inpainting area.
Source and clean images are served through actor-scoped private routes. The result is compared in the browser and downloaded as PNG without publishing it to a public gallery.
RESPONSIBLE USE
Use text removal for your own marketing graphics, templates, photos, screenshots, and authorized client work. Do not use it to strip ownership marks, attribution, legal notices, evidence, signatures, official records, or other text when you lack permission or when removal could mislead viewers. Keep the original file for audit and rollback.
COMMON QUESTIONS
Practical answers about background repair, selective removal, credits, supported image files, PNG downloads, and private processing.
RELATED TOOLS
Use the main image translator when the wording should be replaced with another language instead of erased.
Open the English-focused tool when the desired output is an English image with editable translated text blocks.
Review current image limits, credits, history, batch capacity, and commercial-use options before processing regular workloads.
Upload your authorized image, review the detected text boxes, choose what should disappear, and compare the rebuilt background before downloading the PNG.
Remove Text from Image