# Is there a tool to search video by text description?

> Yes, and there are five different kinds, which is why the recommendation you get from a search engine is so often wrong for your situation.

Canonical URL: https://vivu.ai/guide/is-there-a-tool-to-search-video-by-text

Yes, and there are five different kinds, which is why the recommendation you get from a search engine is so often wrong for your situation. What separates them is where the words you type have to already exist: in the dialogue, in a description somebody wrote by hand, in a fixed vocabulary of detected objects, or nowhere at all until the moment you type them. Work out which of those describes your footage and the choice makes itself.

## If the words were spoken, transcript search covers it

The mature, cheap, well-supported route. Transcribe everything, index the text, search phrases, jump to the timecode. Editors have wanted this for a long time, and people setting up a documentary project will go looking specifically for the phrase-search-across-all-transcripts behavior they remember from an older edit suite. It works well and it has one hard edge: if nobody said it, it is not in there. Silent B-roll, screen recordings, and reaction shots are invisible to this entire category. [Searching by what was said](https://vivu.ai/guide/search-video-by-spoken-words) is worth doing first anyway, because it is the least work.

## If somebody wrote it down, tag and metadata search covers it

Every asset management system lets you search descriptions, keywords, and custom fields. The search is excellent and the bottleneck is upstream: someone had to write the description, and they could only write down what they thought would be needed later. Six months on you are searching for the thing nobody anticipated. This route is strongest for material that gets described as part of an existing process, like a shoot log or a delivery spec, and weakest for the raw pile.

## Automatic labeling gets you a vocabulary, not a sentence

Detection models attach labels to frames: a person, a car, a beach, a laptop. You then search the labels. Coarse filtering works fine, and it is genuinely useful for cutting an archive down before you start looking properly. Where it runs out is specificity. The label set is fixed in advance, so a query like "the take where she picks the mug up with her left hand" has no label to match against, and [what the tagging step can and cannot produce](https://vivu.ai/guide/what-software-can-automatically-tag-video-files) is the thing to check before budgeting around it.

## Natural-language retrieval is the one people mean by this question

This is the category where you type a description of a scene and get moments back, whether or not the words were spoken and whether or not anyone tagged anything. It works by converting the footage and your sentence into the same kind of representation ahead of time, then matching. There is an indexing pass to pay for, and results are ranked by similarity rather than exact, so you get a shortlist rather than one answer. It degrades on abstract descriptions, on anything requiring judgment about intent, and on distinguishing between takes that look nearly identical.

The practical question in this category is what has to happen to your footage first. With [Vivu](https://vivu.ai/platform), the answer is an upload into a private project, where the footage is indexed a single time; new questions after that do not trigger any reprocessing. For a large archive, that upload is the step to plan, so decide early which footage actually needs to be searchable.

## The tools that search other people's video

A large share of results for this query are tools that search public video on the web. They are good at what they do and they have nothing to do with your archive. If you are trying to find a clip you shot, filter these out early.

## When you don't need any of this

If the archive is small enough that someone knows it, that person is the search tool and they are faster than anything you could install. If your material is consistently structured, a numbered episode series or a fixed shot list, then a spreadsheet and a naming convention will outperform semantic search for years. And if you need to find one thing once, scrubbing a timeline is free.

## Deciding which one you are actually shopping for

Take the last three things you went looking for and write out the sentence you would have typed. If the words in those sentences were said out loud, transcript search is your answer and everything else is overbuying. If they describe something visible that nobody narrated, transcripts will never get you there and the choice is between labeling and natural-language retrieval, which comes down to whether your queries fit a fixed vocabulary. If the sentences contain judgments rather than descriptions, no tool in this list is going to satisfy you, and that is worth knowing before a procurement cycle rather than after one. [What happens between your sentence and the result](https://vivu.ai/guide/how-does-ai-search-inside-video-footage) is the part that determines which of those three groups you fall into.

## FAQ

### Can I search my own footage with a description, or only public video?

Both exist, and they are separate product categories. Tools that search public video index what is on the open web and cannot see your drive. Tools that search your own archive have to index your material first, either by having it uploaded to them or by connecting to where it already lives. If you are searching for something you shot, make sure you are looking at the second kind, because the two are easy to confuse from search results alone.

### What kinds of description tend not to work?

Anything requiring judgment rather than observation. "The shot where he looks unsure" and "our most on-brand product close-up" fail, because they encode an opinion rather than a visual fact. Descriptions of specific individuals or products also tend to fail unless the name was spoken out loud or the system was given examples to recognize. Concrete, visible, describable things work best: an object, an action, a setting, text on a screen.

### Does my footage need transcripts before any of this works?

For transcript search, obviously yes. For visual retrieval, no, and that is the point of it, though systems that use both will do better on material that has dialogue. If you already have transcripts, keep them. They are the cheapest coverage you will ever get for spoken content and they compose fine with a visual index.

### Can it find a specific person or product across an archive?

Only sometimes, and it depends on the tool being given something to match against. A generic description of a person gets you people who look roughly like that, not that person. Recognizing a named individual or a specific SKU requires either the name being said in the audio or a reference the system was set up with in advance. Assume it is a separate piece of configuration rather than something you get for free.