Image to Text

Extract text from an image, right in your browser

Overview

Image to Text pulls the words out of an image. Upload, drop, or paste a screenshot, photo, or scan and it runs optical character recognition (OCR) to give you back selectable, copyable text. It is part of the kimtos new-tab app suite.

Everything is fully on-device. There is no backend, no account, and no upload: the OCR engine runs inside your browser, and the image never leaves the machine.

Highlights

• Flexible input — choose a file, drag-and-drop an image onto the dropzone, or paste one straight from the clipboard (⌘/Ctrl+V). Supports PNG, JPG, WEBP, GIF, and more.
• Live preview — the picked image shows in a framed preview before and after extraction, with a one-click view full size lightbox.
• In-browser OCR — text is recognized by Tesseract.js (WebAssembly), with a progress bar that tracks the loading and recognizing steps.
• Readable result — the extracted text appears first, with a word count, and the source image sits just below it.
• Copy & download — copy the text to the clipboard or save it as a snaptext.txt file.
• History sidebar — every extraction is saved locally as a scan. Revisit a past run, re-copy its text, or batch-delete several at once with one-tap undo.
• Language choice — defaults to English; set another Tesseract language code (for example ara, fra, deu) in settings.

Privacy model

Image to Text never sends your image anywhere:

• OCR runs in the browser via a self-hosted Tesseract.js WebAssembly worker. The picture is read on the device and is not uploaded.
• English language data is bundled with the extension, so English OCR works fully offline with no download.
• History (the extracted text plus the original image) is stored only in your browser, and is included in the suite's local backup. Uninstalling the app can erase it.

The model compiles once and is then cached, so later runs are fast and continue to work offline.

Getting Started

Open Image to Text from the dock. The left sidebar holds your past scans; the main pane starts on a fresh input screen, ready for an image.

Provide an image

There are three ways to load a picture:

• Upload — click the dropzone (Drop or paste an image, or click to browse) to pick a file. PNG, JPG, WEBP, GIF, and similar formats are accepted.
• Drag-and-drop — drag an image file from your desktop onto the dropzone.
• Paste — copy an image anywhere, then press ⌘/Ctrl+V while the app is open.

Once loaded, the dropzone is replaced by a preview showing the image, its filename, and size. Hover the preview and click the zoom button to open it full size in a lightbox (click outside or press Esc to close). Click Remove to clear it and start again.

Extract the text

Click Extract text. A progress bar shows the engine's steps — loading the core/language data, then Recognizing text — under the heading Reading the image…. The first run compiles the OCR engine; afterwards it is cached and much faster.

View the result

When recognition finishes, the result pane shows:

• The extracted text first, in a labelled, read-only box with a word count.
• The original image preview just below it (again with the full-size zoom lightbox).

If no text is found, the box reads (no text found).

Copy and download

From the result actions you can:

• Copy — copy the extracted text to the clipboard.
• Download .txt — save the text as snaptext.txt.
• Start over — return to the input screen for another image.

History

Each successful extraction is saved to the history sidebar as a scan (the text plus the source image), newest first.

• Open a scan to view its text and image again.
• Right-click a scan for quick actions — Open, Copy text, Download .txt.
• Delete several — click Select, tick the scans (or drag across them), then Delete (N). A toast lets you undo for a few seconds.

Language settings

Image to Text recognizes English by default. To read another script, open the app's settings and change the Language field to a Tesseract language code — for example ara (Arabic), fra (French), or deu (German). English data ships with the app; other languages are downloaded on first use and then cached.

Architecture

Image to Text is intentionally thin. It is built on the suite's shared media → text factory, so the descriptor declares the copy and settings while the heavy lifting is a single on-device OCR engine. The code lives in extension/src/apps/snaptext/ and extension/src/lib/engines/.

Modules

File	Role
apps/snaptext/index.ts	The app descriptor — id, name, accent, settings (language), and the run hook. Wires the image input to the OCR engine via createMediaTextApp.
lib/createMediaTextApp.ts	The shared "media in → text out" factory — history sidebar, progress bar, result view, copy/download, and on-device data handling.
components/imageInput.ts	The input control — upload, drag-and-drop, and clipboard paste, with a framed preview and validation.
components/imagePanel.ts	The image preview frame with a view full size button.
components/lightbox.ts	The full-screen image viewer (a native modal <dialog>).
components/resultView.ts	The text result card — word count plus Copy, Download .txt, and Start over.
lib/engines/ocr.ts	The OCR engine — runs Tesseract.js (WebAssembly) in a self-hosted worker and returns the recognized text.

Data flow

The image is captured as a blob in the browser, handed to recognizeText, and read entirely on-device. Nothing is uploaded.

The OCR engine

engines/ocr.ts lazy-imports Tesseract.js and creates a worker for the chosen language, then calls worker.recognize(file) and returns the trimmed text.

A few details keep it private and offline-friendly:

• Self-hosted worker + core. The worker script (/ocr/worker.min.js) and the WASM core (/ocr/tesseract-core-simd-lstm.wasm.js) are copied into the extension and loaded from root-relative paths. Manifest V3 forbids loading a worker from a CDN, and its CSP bans blob: workers, so workerBlobURL is set to false and the worker file is loaded directly.
• Bundled English data. For eng, the language data (/ocr/eng.traineddata.gz) is bundled and pointed at via langPath, so English OCR runs with no download. Other languages fall back to the Tesseract CDN on first use and are then cached.
• Progress reporting. A logger normalizes Tesseract's status into a { phase, label, ratio } shape — download while loading the core/language, recognize while reading the image — which drives the progress bar.

A companion prefetchOcr helper warms the engine (compiles the WASM and loads the bundled English data) ahead of time, so the first real recognition is instant.

Why on-device

Because the worker, core, and English data are all served from the extension and run as WebAssembly in the browser, Image to Text needs no server and no network for English. The model downloads (when needed) once and is cached; after that it runs offline, and your image stays on the device.