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UMI-OCR / MAKE TEXT IN IMAGES USEFUL AGAIN
Umi-OCR
A free offline desktop OCR application for screenshots, batches of images and scanned documents. Umi-OCR brings recognition engines into a practical interface with editable results, layout handling and searchable PDF workflows.
Official Umi-OCR repository screenshot showing screenshot recognition, editable text and formatting controls; interface exampleExplore the image ↓
What you can do with it
What this tool is for
Umi-OCR turns text trapped inside screenshots and scanned documents into material that can be searched, copied and edited. Its main advantage is a desktop workflow: users can capture a region, inspect the recognition result and continue working without building a Python application or buying cloud OCR credits.
The project supports Windows and Linux x64, with platform-specific installation instructions. It packages recognition through engine plugins, including RapidOCR and PaddleOCR options. The interface is not itself a new recognition model, so language coverage, accuracy and available specialized features depend on the chosen engine and model.
01
Recover meeting-slide text
02
Digitize a scanned archive
03
Read a software error screenshot
04
Build a searchable reference folder
A closer look at the product
Official product image. Click to inspect the details.
Product interface and examples
Key features, practical tasks
Capabilities in practice
- Screenshot recognition: Use a shortcut or paste an image to recognize a selected region. This is useful for slides, application dialogs and image-only web content where ordinary text selection is unavailable. Check the result before pasting it into another document.
- Batch image processing: Queue multiple supported image files rather than repeatedly using screenshot mode. Keep file names meaningful so exported text can be traced back to the original page when an ambiguous character needs a second look.
- Layout postprocessing: Adjust how recognized lines become paragraphs, columns or preserved line breaks. Code screenshots and prose need different handling: a tidy paragraph can damage indentation, while preserving every line break can make an article difficult to edit.
- Document workflows: Process supported document formats such as PDF and choose between existing-text extraction and OCR when appropriate. Searchable PDF output helps retain the page appearance while adding a text layer; it does not recreate a fully editable Word layout.
- Ignore regions: Exclude recurring areas such as headers or unwanted margins from recognition. This helps reduce repeated noise in a batch. It changes what OCR reads, not the ownership or underlying pixels of the source image.
- Useful export formats: The project documents plain text, JSONL, Markdown and CSV options for image results. Choose a structured format for downstream processing and readable text for manual review; do not assume a CSV export automatically reconstructs every table.
- Automation interfaces: Command-line and HTTP documentation provide routes for integrating desktop recognition into a larger workflow. A developer can automate repeated input while retaining the same local engine, rather than depending on a separate hosted OCR provider.
Get started, step by step
- 01
Install the correct package
- 02
Choose a representative sample
- 03
Recognize and inspect
- 04
Export with traceability
A practical first workflow
A practical first workflow
- Install the correct package. Follow the official instructions for your operating system and choose the required engine plugin. Keep the application and model files together as documented rather than moving individual executables out of the package.
- Choose a representative sample. Use one ordinary page and one difficult page from the same collection. Evaluate small text, mixed languages and any recurring headers before committing to a large import.
- Recognize and inspect. Select the layout handling and output format suited to the task. Compare suspicious characters with the image, especially prices, identifiers and punctuation that may look visually similar.
- Export with traceability. Save results using recognizable file names and retain the source images. For a repeated job, record the engine and important settings so future batches can be processed consistently.
Example prompt or task: Recognize this batch of scanned meeting notes locally. Preserve page order, keep technical abbreviations unchanged where possible, and review dates and action-owner names against the original images.
This is an editorial starting workflow, not a measured performance result.
Find the right plan for you
Free software
Explore official plans ↗Free software Models, hardware and hosting are separate
Umi-OCR is free open-source software under the MIT license. Local recognition does not require purchasing a per-page cloud plan, but it still uses the computer's processor, memory, storage and electricity.
What is free and what can cost extra
Umi-OCR is free open-source software under the MIT license. Local recognition does not require purchasing a per-page cloud plan, but it still uses the computer's processor, memory, storage and electricity.
The application supports different engine plugins, whose models and dependencies must be available on the machine. Optional external integrations are separate products. Free software should not be interpreted as a promise of unlimited speed, perfect recognition or maintenance support.
A few things worth knowing
Frequently asked questions
Is Umi-OCR free?
Yes. The application is published under the MIT license.
Can it work offline?
Local recognition can run offline once the required program, engine and models are installed.
Does it support screenshots?
Yes. Screenshot recognition is one of its main desktop workflows.
Can it process many images?
Yes. Batch processing is documented, with practical limits determined by your machine and workload.
Does searchable PDF mean editable Word?
No. A searchable PDF adds usable text while retaining a PDF-oriented document workflow.
Is macOS officially listed?
The current project targets Windows x64 and Linux x64; check future releases separately.
Is it different from RapidOCR?
Yes. Umi-OCR is a desktop application; RapidOCR is one of the recognition engines that can power it.
The details behind your decision
Explore platforms, inputs and outputs, licensing, and access requirements.
Four useful starting projects
Four useful starting projects
- Recover meeting-slide text: Capture a slide containing several action points and convert the result into editable notes. Compare names, dates and bullet boundaries with the slide before sharing the notes with colleagues.
- Digitize a scanned archive: Start with a few pages from different parts of the collection. Identify rotation, low contrast and mixed layouts before selecting settings for the full batch, then keep the original images beside the extracted text.
- Read a software error screenshot: Preserve line breaks and verify punctuation in paths and command names. OCR can save typing, but one changed character can make a copied command or configuration entry incorrect.
- Build a searchable reference folder: Create searchable PDFs for documents that are currently image-only. Confirm that selecting and searching text works in the intended PDF reader and that page order remains unchanged.
Platforms, inputs, outputs and setup
Platforms, inputs, outputs and setup
Windows users can follow the portable-package route, while Linux users should follow the project's current platform instructions. The documented desktop targets are x64; do not assume a native macOS or mobile build from the existence of a web-style interface.
Keep a clean source image when possible. OCR locates and recognizes visual text; complex tables, handwriting, mathematical notation and decorative fonts may need different models or manual correction. Model support should be checked before planning a specialized document project.
Data, permissions and licensing
Data, permissions and licensing
Offline OCR is useful for keeping documents on your own machine, provided the configured processing route is local. Download models and updates from official sources, and review any external service you deliberately connect.
Before exporting a shared archive, check whether recognized text exposes information that was difficult to search in the original scans. Keep only the records needed for the task and apply the same access controls to text exports as to source documents.
Sources, documentation and help
Official sources and review date
Official sources and review date
Reviewed October 3, 2026. Product facts come from the official sources below. Suggested projects, prompts and review methods are FindGoodAI editorial guidance, not measured performance results.
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