A good caption tool gets names and numbers right, keeps captions in sync with speech, exports the file format your platform needs, and lets you apply a brand style without re-doing it for every video. This checklist turns those requirements into pass/fail checks you can run on any tool in about an hour, using one of your own videos.
I review AI video tools for creators, marketers and small teams, and the same mistake keeps showing up. People compare caption tools by the demo clip on the homepage, then discover on the third video that the tool mangles product names or can’t export what LinkedIn or their website player needs. Test on your own footage, with written criteria, before you commit.
TL;DR
- Measure accuracy on your own footage against a reference transcript. The common target for finished captions is 99% accuracy, and raw automatic captions rarely reach it without a human pass.
- Check readability limits: two lines at most, roughly 32 to 45 characters per line, captions on screen long enough to read.
- Choose the output by destination: soft captions (SRT or VTT) for real players, burned-in captions for muted social clips.
- Use a dedicated caption pass when brand styling, word-level timing and clean exports matter. A full editor is enough for light, one-off captioning.
- Write a one-page caption brief and a QA pass, so every video gets the same result.
Table of Contents
- What a caption must do before you judge any tool
- The caption tool evaluation checklist
- How to test a tool in one hour
- SRT, VTT or burned-in: choosing the output
- Dedicated caption tool or your video editor?
- A caption workflow checklist
- A caption design brief you can copy
- Our perspective
- Find a caption tool that fits your workflow
- FAQ
- Sources
What a caption must do before you judge any tool
A tool is only as good as the standard you hold its output to. Start with the standard, then test the tool against it.
The University of Arizona’s accessibility guide ties captions to WCAG 2.1 AA and sets practical targets: 99% accuracy, captions appearing within one second of speech, two to three lines maximum per frame, and 32 to 42 characters per line. Its checklist also covers verbatim speech, speaker identification, correct spelling of names and technical terms, descriptions of non-speech sounds, line breaks at natural phrases, and display time long enough to read.
Section508.gov, the U.S. federal accessibility guidance, adds that captions should be synchronized with the audio, include all dialogue and important sounds, use correct spelling and punctuation, and follow a consistent style for speakers, sound effects and music.
The W3C Web Accessibility Initiative is blunt about automation: automatically generated captions do not meet user needs or accessibility requirements unless they have been confirmed to be fully accurate. So the first question for any AI caption tool is not how good its transcript is. It is how fast you can review and correct it.
The caption tool evaluation checklist
Score each line pass, partial or fail. A tool that fails the first two sections is not worth testing further, whatever its price.
Accuracy and editing
- Names, brands and jargon. Does it get your company name, product names, acronyms and guest names right? Can you add a custom vocabulary or fix a term once and apply it everywhere in the file?
- Numbers, prices and URLs. Are figures, dates and web addresses transcribed correctly?
- Speaker handling. Does it separate speakers when two or more people talk? Can you label them?
- Non-speech cues. Can you add sound descriptions such as music or laughter where they carry meaning?
- Editor quality. Can you edit text and timing in one view, play the clip against the captions, and fix a line without breaking neighboring timing?
- Accents and audio quality. Test with your real recordings, including a noisy one, not only a clean studio take.
Timing and readability
- Sync. Captions should appear within about a second of the speech, as the Arizona guide specifies.
- Word-level timing. Needed for highlighted, word-by-word styles common in short-form video.
- Line control. Does the tool break lines at natural phrases and respect a maximum characters-per-line setting you can change?
- Display time. Short lines must stay up long enough to read. Check the fastest-talking section of your clip.
Styling and brand control
- Reusable presets. Font, size, color, background and position saved as a template, not re-entered per video.
- Safe placement. Can you keep captions out of platform interface areas and away from on-screen text or lower thirds?
- Contrast. Section508.gov suggests a default of a sans-serif font such as Helvetica or Arial at 18 points, white text on a translucent black background, no more than two lines, and no more than 45 characters per line, centered in the lower third. Use it as a baseline and deviate on purpose.
Output and compatibility
- Formats. SRT and VTT export, plus a burned-in MP4 option if you publish to social.
- Export quality. Resolution and frame rate should match the source. Check for watermarks on the plan you would actually use.
- Batch and speed. How long does a 10-minute video take from upload to export? How does it handle ten videos in a row?
- Languages. If you publish in more than one language, check whether you can produce a separate track per language. Our guide to multi-language subtitles covers the workflow.
Pricing clarity
- Does the plan you need include the exports and minutes you will use?
- Do failed or repeated renders cost extra? Retries can multiply what you pay on some tools, which we cover in our note on AI video editor pricing.
How to test a tool in one hour
The test is simple, and it works because it uses your footage rather than the vendor’s.
- Pick a sample. Choose a two-to-three-minute clip with your typical difficulty: product names, numbers, two speakers, some background noise.
- Build a reference. Transcribe the clip yourself, or correct a draft carefully until it is right. This is your answer key.
- Run each tool on the same clip with default settings, then again after applying a custom vocabulary if the tool offers one.
- Count errors. A word that is wrong, missing or added counts as one error. Accuracy is total words minus errors, divided by total words, times 100. Weight errors that change meaning more heavily than a missing “the”.
- Check the meaning errors separately. The W3C WAI warns that a single missing word such as “not” can make captions contradict the audio. A tool with 97% accuracy and no meaning errors can beat one with 98% and a reversed sentence.
- Time the correction. Record how many minutes it takes to bring the file to publish quality. This number matters more than the raw score, because you will pay it on every video.
- Export and play. Export in every format you need and open each one in the actual destination: your website player, YouTube, LinkedIn, Instagram. Watch on a phone with the sound off.
If you want a starting shortlist, our rankings of AI caption generators and AI subtitle generators are scored on this kind of workflow fit.
If your scripts, titles or descriptions also come from AI writing tools, a guide to humanizing AI-generated text helps keep the written voice consistent with the captions you correct by hand.
SRT, VTT or burned-in: choosing the output
The format question decides how your captions behave after publishing, so settle it before you pick a tool.
| SRT / VTT (soft captions) | Burned-in (open) captions | |
|---|---|---|
| What it is | A separate text file with timing | Text rendered into the video pixels |
| Viewer can turn off | Yes | No |
| Fixing a typo | Edit the text file | Re-export the video |
| Multiple languages | One file per language | One video per language |
| Works when a player ignores tracks | No | Yes |
| Best for | Websites, YouTube, learning platforms, anything accessibility-driven | Muted autoplay, social clips, background loops |
The W3C notes that WebVTT is the most common caption format on the web, with SRT and TTML also in common use. SRT is the simpler of the two, while VTT adds styling and positioning options for web players. We keep an SRT as the master file and derive VTT from it, which is the approach in our SRT vs VTT rules.
Where the platform shows only the video, burned-in captions are the practical choice. Our burned-in captions guide covers when to use them, and the LinkedIn captions workflow shows a two-layer setup: a burned-in MP4 for the feed plus an SRT for accessibility and editing.
The accessibility point is worth stating plainly. Burned-in text cannot be toggled or restyled by the viewer. For videos where accessibility compliance matters, publish a soft caption track as well.
Dedicated caption tool or your video editor?
For brand videos, the question is not which is better in general but which step each handles well.
A video editor with built-in auto captions is enough when:
- you caption a handful of videos a month,
- you already do the whole edit in that editor,
- your caption styling is simple, and
- you do not need to export separate caption files.
A dedicated caption pass earns its place when:
- you need word-level timing and consistent brand presets across many videos,
- you need SRT or VTT files alongside the video,
- several people review captions, or
- you caption in multiple languages.
The workflow that holds up best is two steps: finish the picture edit first, lock it, then caption. Captioning before the cut is final means redoing timing every time a clip moves. If you want to compare editors on caption quality, see our roundup of the best video editor auto captions. For short-form, this auto subtitles workflow for Shorts shows how fast the cycle can run.
A caption workflow checklist
Use this as the repeatable version of your test. It works for a solo creator and scales to a team.
- Lock the picture. No more cuts after captioning starts.
- Generate the draft with your chosen tool, with speaker detection on if there are multiple voices.
- Review in a fixed order: names and jargon, numbers, speaker changes, sound cues, line length, reading speed, timing.
- Apply the caption style from your brief.
- Export the master SRT, then the destination files: VTT, burned-in MP4, or both.
- Validate on devices. Play the final file on at least one phone and one desktop, sound off and sound on.
- Archive. Keep the SRT with the project, named consistently with the video and a version number.
The order in step 3 matters. Fix meaning first, then fit. A line that reads well but is wrong is worse than a clumsy line that is right.
A caption design brief you can copy
A brief stops caption style from drifting between videos and editors. One page is enough. Include:
- Font and size: one sans-serif typeface and a minimum size that stays legible on a phone.
- Color and contrast: text color, background box or outline, and a rule for light footage.
- Position: default placement in the lower third, plus where to move captions when on-screen text appears.
- Line rules: a maximum of two lines and a characters-per-line limit. Section508.gov’s guidance is no more than 45 characters per line.
- Speaker and sound conventions: how to label speakers, music and sound effects, applied the same way every time.
- Terminology list: product names, people’s names, acronyms and their approved spelling.
- Emphasis style: whether you highlight keywords or the active word, and how often.
- Accessibility rule: which videos need a soft caption track in addition to burned-in text.
As the Arizona guide notes, line breaks should fall at natural phrases and captions should stay up long enough to read. Put both in the brief as review criteria.
Our perspective
We built TechVideoBlog because caption tools are hard to compare from their own marketing. Every tool shows a clean demo clip. Your footage has product names, crosstalk and a noisy room.
That is why we judge tools on workflow fit: how quickly a rough transcript becomes a publishable file, how reliably brand styling carries across videos, and whether the exports match where the video lives. Pricing clarity counts as much as output quality, because a tool you cannot predict is a tool you cannot budget.
Our directory is organized so you can narrow by use case, such as Shorts, podcast clips or LinkedIn video, and then run the checklist above on a short list instead of on everything. The checklist and our scoring point the same way: measure on your own footage, and keep the review pass fast.
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Find a caption tool that fits your workflow
If you would rather start from a tested shortlist than from a blank page, our directory covers caption generators, subtitle tools, editors and repurposing tools, with verified pricing and workflow notes for each.
Browse the full tools directory, or go straight to the best AI video editors if you are choosing one tool for both editing and captions.
FAQ
What accuracy should I expect from a caption tool?
The common target for finished captions is 99% accuracy, which is the level the University of Arizona’s guide uses. Automatic output usually needs a human review to reach it. Measure it on your own clip rather than relying on a vendor figure.
Should I use SRT, VTT or burned-in captions?
Use a soft caption file, SRT or VTT, wherever the viewer watches in a real player, because it can be toggled, edited and translated. Use burned-in captions for muted social feeds where the platform shows only the video. Many creators do both: a burned-in version for social and an SRT for everything else.
Is my video editor’s auto caption feature good enough for brand videos?
It can be, if you caption a few videos, your style needs are simple and you do not need separate caption files. When you need consistent brand presets, word-level timing or multi-language tracks, a dedicated caption pass saves time. Run the one-hour test on both and compare correction time.
How many lines and characters should a caption have?
Keep captions to two lines at most. Guidance varies slightly: Arizona suggests 32 to 42 characters per line, and Section508.gov recommends no more than 45. Pick one limit in your brief and apply it everywhere.
What should I check before publishing captions?
Check names and jargon, numbers, speaker changes, sound cues, line length, reading speed and timing, in that order. Then play the exported file on a phone and a desktop in the destination platform. Pay special attention to any missing word that could reverse the meaning.
Sources
- Video Captioning Guide, University of Arizona Accessibility
- Captions and Transcripts, Section508.gov
- Captions/Subtitles, W3C Web Accessibility Initiative
- How to Humanize AI Text with Instructions
