What's the difference between searching your footage and transcribing it?
Transcription turns speech into text you can search; visual search turns the picture into something you can search. They fail on opposite material: a transcript is useless for a silent drone shot, and visual search will not find the moment someone said the word "quarterly".
Published September 10, 2026 · Field notes
People use "searching my footage" for both, which hides that they index different signals and fail on opposite material. Getting the distinction right saves you from setting up the wrong one and concluding that AI search does not work.
What each one actually indexes
| Transcription | Visual search | |
|---|---|---|
| Input signal | The audio track | Sampled frames of the picture |
| Produces | Timestamped text | One vector per sampled frame |
| You search with | Words that were said | A description of what was visible |
| Precision | Exact - a word either occurs or it does not | Ranked similarity, no exact matches |
| Fails on | Silent footage, ambient audio, music | Anything defined by speech; fine detail; identity |
| Typical index size | Kilobytes of text per hour | A few KB per sampled frame |
Why transcription is more precise when it applies
Language is dense. One sentence of speech contains proper nouns, numbers and specifics that no visual description can match: "the Rotterdam pilot came in eleven percent under" is unmistakable and findable forever. A picture of a meeting room is a picture of a meeting room. When your material has speech, the transcript is almost always the more valuable index, and it is the one to build first.
It is also exact. Text search returns matches, not rankings - the word is there or it is not - so you can trust a negative result. That property matters more than people expect: with ranked visual search you can never be sure the clip is absent, only that it did not rank.
Why visual search is the only option for B-roll
Roughly speaking, the footage people say they lose is footage nobody talked over. Drone passes, cutaways, establishing shots, product spins, stock clips with names like 8348320-uhd_3840.mp4 - an editor in r/editors described exactly this pile, about 100GB of stock, renders and drone shots, half of them named by a stock site. Transcribing them produces empty files.
Visual search works on that material because it does not need anyone to have said or typed anything. A vision model turns each sampled frame into a vector, your query goes through the text half of the same model, and the nearest vectors come back. "Aerial drone shot" and "product on white background" work without a single tag existing anywhere.
The trade is that similarity is fuzzy. You get a ranked list, weak matches are still matches, and fine-grained things - which of two similar rooms, the text on a sign, which person - are where these models are weakest.
Sampling: the limit that surprises people
Visual indexes do not look at every frame; at 30fps that would be 108,000 embeddings per hour. They sample. Where the samples land determines what is findable, and anything between samples is not in the index.
Reelary samples at shot boundaries where it can detect them - it runs a scene-change pass first so that samples fall inside a single shot rather than across a cut - and otherwise every 2 seconds, with a hard cap of 16 sampled frames per video. For a 30-second B-roll clip that is dense coverage. For a two-hour recording, 16 samples is a summary. This is a deliberate trade for index speed, and it is the single most important thing to know before pointing it at long-form material.
Most libraries need both, for different halves
A documentary archive is the clearest case: the interviews want a transcript, the B-roll wants visual search, and they are the same project. A wedding videographer wants visual search for the day and a transcript for the speeches. A streamer wants a transcript for what was said and visual search for the moment the room lit up.
Where the two get combined into one search box, be careful about which half is actually running. Resolve 21's IntelliSearch, for example, lets you choose visuals, transcribed audio or metadata - which is the honest interface for this, because the three behave differently. See can DaVinci Resolve search your footage.
Where Reelary sits, plainly.
Reelary's library search is visual only. It includes local speech-to-text, but that is used to generate captions for the cut you are assembling - it does not build a searchable transcript index across your library, and dialogue search is not a feature you can rely on today. If what you need is to find the moment somebody said a particular word across an archive, use a transcription tool, and do not buy this one expecting that. If what you need is to find a silent shot you cannot name, that is exactly what it is for.
Questions people ask about this
Is transcription the same as video search?
No. Transcription converts speech to timestamped text, so it finds moments where something was said. Visual search embeds sampled frames so it finds moments where something was visible. A silent drone shot is invisible to a transcript, and a spoken product name is invisible to visual search.
Which one should I set up first?
Whichever matches your material. Interviews, podcasts, streams, lectures and meetings: transcribe first, because speech is far more specific than any description of a picture. B-roll, drone, stock, event coverage and product footage: visual search first, because there is often no speech at all.
Can visual search find text on screen or a specific person?
Usually not reliably. Frame-embedding models match the overall content of a frame, so they are good at subject, setting, colour and time of day and weak at reading signage, distinguishing people by identity, or catching detail that occupies a small part of the frame. Optical character recognition and face recognition are separate capabilities.