TL;DR: Subtitle Edit recently added Video OCR (Video → OCR burned-in subtitle…), so this free, open-source editor can now pull hardcoded subtitles out of a video — a genuinely useful addition. The difference is under the hood. Subtitle Edit asks you to draw a scan rectangle and then runs a general-purpose OCR engine frame by frame (Apple Vision on macOS; Paddle OCR on Windows/Linux; or downloadable vision models like GLM-OCR and PaddleOCR-VL). GeekLink instead detects the actual subtitle lines automatically and recognizes them with models purpose-tuned for subtitle text — which makes it much faster and less likely to miss a line — and it ships ready to run, with no OCR engine or model for you to download or install. If you already live in Subtitle Edit and want free OCR inside your editor, its Video OCR is a solid option. If you want the fastest, most hands-off burned-in-subtitle extraction, that is where GeekLink pulls ahead.
Want burned-in subtitles extracted with nothing to install? GeekLink finds the subtitle lines automatically and runs OCR locally on your computer — no engine to pick, no model to download. Free tier, no account required.
Download FreeWhat is Subtitle Edit's Video OCR?
Subtitle Edit is a long-standing, free and open-source subtitle editor, and it recently gained a Video OCR feature that extracts burned-in (hardcoded) subtitles from a video into editable lines. You open Video → OCR burned-in subtitle…, find a frame with a subtitle, adjust a scan-area rectangle over where the subtitles sit (the bottom third by default), pick an OCR engine and language, optionally test the current frame, then click Start OCR. Recognized lines appear in a list you can edit in place. For a tool many people already use for timing and cleanup, having OCR built in is a real convenience.
What matters for this comparison is how it recognizes text. Subtitle Edit hands the job to a general OCR engine and, per its own documentation, the choice depends on your platform:
- Apple Vision — macOS only; local, fast, and nothing to download.
- Paddle OCR — Windows and Linux; local, downloaded automatically.
- llama.cpp vision models — GLM-OCR 0.9B, PaddleOCR-VL 1.6 (109 languages), HunyuanOCR, LightOnOCR; cross-platform, engine and models downloaded automatically.
- CrispEmbed and Ollama vision — other local vision-model paths (Ollama installed separately), plus a cloud GLM API option.
These are capable, general-purpose recognizers — the vision-model options in particular are strong on raw characters and cover a lot of languages. But they are pointed at a rectangle and run on sampled frames; the subtitle-specific work — deciding which lines are actually subtitles, ignoring logos and on-screen clutter, and not re-reading the same caption on every frame — is left to you and to how well you drew the box.
GeekLink vs Subtitle Edit Video OCR: How do they compare?
Both read burned-in text into editable subtitles. The gap is in the four things GeekLink builds specifically for subtitles: automatic line detection, subtitle-tuned recognition, speed, and zero model setup.
| Feature | GeekLink | Subtitle Edit Video OCR |
|---|---|---|
| Burned-in subtitle OCR → editable subtitles | Yes — core feature | Yes — newer built-in feature |
| Finding the subtitle lines | Automatic subtitle-line detection — finds each line, including the sparser top line of two-line subtitles | You draw a scan rectangle; a general engine OCRs frames inside it |
| Recognition model | Models purpose-tuned for on-screen subtitle text | General-purpose engines (Apple Vision / Paddle OCR / vision LLMs) |
| Model download / setup | None — ships ready to run, models prepared for you | Depends on engine — Paddle OCR and vision models are downloaded (some large); Ollama installed separately; Apple Vision needs nothing |
| Speed | ~3 min for a 30-minute 1080p English-subtitle video on an Apple M4 Mac with 16 GB RAM, default settings (~10× real-time) | Varies widely by engine; Apple Vision is fast, while accurate vision-model engines are heavy per frame (slower without a fast GPU) |
| Ignoring logos, watermarks, on-screen text | Filtered automatically outside the subtitle lines | Whatever falls in the scan box is OCR'd; you re-crop to exclude it |
| Platform | Mac and Windows desktop apps | Windows / Linux / macOS (engine availability differs per OS) |
| Price | Free tier for OCR; paid plans available — see the homepage for current pricing | Free, open-source |
It finds the subtitle lines for you
The biggest difference is that GeekLink treats extraction as a subtitle problem, not a rectangle-of-pixels problem. Subtitle Edit's Video OCR asks you to position a scan box and then recognizes whatever text appears inside it on each sampled frame. That works, but it puts the judgment on you: if the box is a little too tight, the sparser top line of a two-line subtitle can fall outside it; if it's too loose, a channel logo, a watermark, or on-screen graphics get read as subtitles.
GeekLink detects the actual subtitle lines first — before recognition — and is deliberately built to over-detect rather than miss (a line you don't want is one click to delete; a line that was never captured is a silent loss). It holds onto the low-frequency lines that a fixed crop tends to drop, and keeps recognition off the logos and clutter. You don't tune a rectangle to protect against those cases; the detection handles them.
It recognizes with subtitle-tuned models
GeekLink's recognition is tuned specifically for on-screen subtitle text — the styled, outlined, sometimes small type that sits over moving footage — rather than a general document-OCR or a general vision model applied to a video still. That tuning is where accuracy on real subtitles comes from: fewer garbled characters on stylized captions, and steadier results on dense CJK text.
Subtitle Edit's engines are general-purpose by design. Apple Vision is quick and needs no download, but it's a general system OCR, and it can struggle on stylized or small CJK subtitles. The vision-model engines (GLM-OCR, PaddleOCR-VL, and friends) are genuinely strong recognizers and cover many languages — but they're general models you point at a frame, not a subtitle-specific pipeline, and, as noted below, they're the heavy, download-first options.
It's much faster
On an Apple M4 Mac with 16 GB RAM, GeekLink extracts a 30-minute 1080p video with English hardcoded subtitles in about 3 minutes with default settings — roughly 10× real-time. Part of that is not re-doing work: a subtitle stays on screen across many consecutive frames, so recognizing every sampled frame independently repeats the same read over and over. GeekLink combines the evidence for a given subtitle and skips frames where nothing changed, so the expensive recognition runs far fewer times.
Subtitle Edit doesn't publish a throughput figure, and honestly it varies a lot by engine. Apple Vision is fast. But the accurate options — the llama.cpp vision models, CrispEmbed, Ollama — are heavy vision LLMs run per frame, which can be slow, especially without a fast GPU. So on Subtitle Edit you often trade speed against accuracy at the point where you pick the engine; GeekLink aims to give you the tuned-accuracy path and the fast path at once.
Nothing to download or install
GeekLink ships ready to run. There's no OCR engine to choose and no model to fetch before you can start: open the app, point it at the video, and go. The recognition models are prepared for you.
With Subtitle Edit's Video OCR, model setup depends on the engine you pick. Paddle OCR downloads automatically on Windows and Linux; the vision-model engines download an engine plus a model (some of them large); Ollama is a separate install you manage yourself. On macOS, Apple Vision is the one path that needs nothing — but it's also the most general recognizer, so on tougher, stylized, or CJK subtitles you're nudged toward the download-first vision models to get the quality up. GeekLink avoids that fork entirely: one path, tuned, nothing to download.
When is Subtitle Edit the better choice?
Plenty of the time. Subtitle Edit is a superb, free, open-source subtitle editor, and its OCR is a real asset:
You're already editing in Subtitle Edit. If your subtitles live in Subtitle Edit for timing and cleanup, doing the OCR in the same window — then editing the recognized lines right there — is convenient and costs nothing.
You want free and open-source, full stop. Subtitle Edit is free and open. If price and openness outweigh speed and hands-off setup, it's a great answer.
You want to pick a specific OCR engine. Subtitle Edit lets you choose among several engines and swap in a particular vision model (or a cloud engine) if you have a reason to prefer one.
The two aren't mutually exclusive, either: a common pattern is to do the fast, hands-off extraction in GeekLink and take the result into Subtitle Edit for detailed timing work. Different tools, different strengths.
Frequently Asked Questions
Does Subtitle Edit have OCR for burned-in subtitles?
Yes. Subtitle Edit recently added a Video OCR feature (Video → OCR burned-in subtitle…) that extracts hardcoded subtitles from a video into editable lines. You draw a scan rectangle, pick an OCR engine and language, and start OCR. GeekLink does the same job but detects the subtitle lines automatically and uses subtitle-tuned models, so there's no rectangle to tune and no engine or model to download.
Which is faster at OCR, GeekLink or Subtitle Edit?
It depends on the Subtitle Edit engine. Apple Vision is fast; the more accurate vision-model engines (llama.cpp with GLM-OCR or PaddleOCR-VL, CrispEmbed, Ollama) are heavy per frame and can be slow without a fast GPU. GeekLink extracts a 30-minute 1080p English-subtitle video in about 3 minutes on an Apple M4 Mac with 16 GB RAM (roughly 10x real-time), partly by combining repeated frames and skipping unchanged ones instead of re-reading every frame.
Do I have to download models to use Subtitle Edit's Video OCR?
For most engines, yes. Paddle OCR downloads automatically on Windows and Linux, and the vision-model engines download an engine plus a model (some large); Ollama is installed separately. On macOS, Apple Vision needs no download but is a general recognizer. GeekLink ships with its recognition models prepared, so there's nothing to pick or download before you start.
Why does GeekLink miss fewer subtitle lines?
Subtitle Edit OCRs whatever text falls inside the scan rectangle you draw, so a slightly tight crop can drop the sparser top line of a two-line subtitle, and a loose crop can pick up logos or on-screen text. GeekLink detects the subtitle lines automatically and is built to over-detect rather than miss — you delete an unwanted line in one click, but a line is never silently lost — and it keeps recognition off watermarks and clutter.
Is Subtitle Edit's Video OCR accurate?
It can be, especially the vision-model engines, which are strong general recognizers across many languages. The trade-offs are that the fast option (Apple Vision) is a general OCR that can struggle on stylized or small CJK subtitles, while the accurate options are heavy downloads run per frame. GeekLink uses models tuned specifically for on-screen subtitle text, aiming for that accuracy without the speed or setup penalty.
Can I use both together?
Yes. A common pattern is to extract burned-in subtitles quickly in GeekLink with nothing to install, then open the result in Subtitle Edit for detailed timing and cleanup. They complement each other — GeekLink for fast, hands-off extraction; Subtitle Edit for a free, deep editing environment.