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You’d think transcripts would solve everything.
Run speech-to-text, get clean captions, and suddenly you’ve got every word from your video nicely typed out. Except now you’re looking at this giant wall of text… and it feels a bit like that moment:
Because transcripts tell you what was said, not what any of it means.
Is Apple a fruit or a company?
Is Jordan a person, a country, or the guy who always forgets to unmute?
Is Paris referring to the city… or the celebrity?
Transcripts don’t help with that. They just give you words. Lots of them. Enough to make anyone whisper, “This… is not helpful.”
We see this everywhere:
• A product team trying to build video search but unsure what they’re supposed to index.
• An OTT platform drowning in thousands of hours of content but unable to organize by speaker or topic.
• A content studio that wants to recommend videos intelligently, but has the metadata equivalent of an empty spreadsheet.
Everyone eventually hits the same wall:
You have raw text, but no structure. No meaning. No idea who or what is being mentioned.
That’s where entity-level classification comes in. In NLP, it has a name, Named Entity Recognition (NER). It’s the thing that finally teaches your transcripts how to make sense.
But we’re not here to drop acronyms on you. We’re here to show how NER works inside video, how it tags people/places/brands automatically, and how it turns messy transcripts into something searchable, recommendable, and actually useful.
Let’s break it down.
Let’s say you’re building a search feature for your video app. You’ve got transcripts. You’ve got basic metadata. And you’re using a general classifier to tag each video by topic, sports, news, entertainment, tech. It’s working... sort of. Until someone asks:
Can we show all the videos where LeBron James talks about Golden State in LA?
Now you're stuck. General classification only tells you what the video is broadly about.
It doesn’t tell you who was mentioned, what brands came up, or where anything happened especially not when in the video those things were said.
Take this line from a transcript:
After the trade, LeBron James said the Lakers were ready to take on Golden State in LA.
Your current stack might tag the entire video as:
{ "topic": "sports" }
But what you really need is this:
[ { "text": "LeBron James", "type": "Person" }, { "text": "Lakers", "type": "Organization" }, { "text": "Golden State", "type": "Organization" }, { "text": "LA", "type": "Location" }]
That’s the shift from general classification to entity-level classification, where you don’t just label the whole video, you label entities inside the transcript. That means:
Once your transcript starts emitting structured entity data, everything else, search, discovery, personalization, becomes a lot easier to build. Next up, we’ll look at what this process is formally called, how it works under the hood, and how it ties into your video workflows.
The formal name for what we’ve been describing is Named Entity Recognition, or NER. It’s a fundamental task in NLP, and one of the most useful when dealing with video transcripts at scale.
NER scans unstructured text and identifies spans that refer to real-world entities. Each entity is classified into a type, such as:
NER works at the token and phrase level, detecting the who, what, and where within every line of dialogue. For developers, it’s best understood as a structured prediction task: for each token (word), the model assigns a label using contextual information.
Most modern NER systems use transformer-based architectures (like BERT) that understand the sequence holistically, not just word-by-word. This allows them to resolve ambiguity and handle edge cases (e.g., “Apple” as a company vs fruit) more accurately than rule-based approaches.
In the context of video, NER is what transforms speech-to-text output into actionable metadata. So while “Named Entity Recognition” might sound academic, in practice, it powers some of the most critical features in modern video platforms.
In the next section, we’ll walk through how it actually works inside a real video pipeline, and how FastPix handles it end-to-end.
If you’re working with hundreds (or thousands) of videos, you need more than just raw captions. You need a way to understand what’s being said: who’s speaking, which brands are mentioned, and where things happen.
At its core, NER teaches machines to read transcripts with context. It’s how a model understands that Apple refers to a company, not a fruit, and that California is a location, especially when it shows up in the middle of a product launch video. It’s not just dictionary matching. It’s context-aware classification of real-world entities.
Before deep learning, this kind of tagging relied on rules and keyword lists. You’d write regex patterns, maintain dictionaries of known people or companies, and try to keep them updated. But video transcripts are messy, full of incomplete sentences, slang, speaker shifts, and barely any punctuation. Rule-based methods fall apart quickly.
Today, NER is powered by transformer-based models like BERT, spaCy, and other systems in the HuggingFace ecosystem. These models use context from the full sentence, sometimes multiple sentences to decide what a given word actually refers to.
And under the hood, the process looks something like this:
From raw text to entity classification the core steps in modern NER.
The NER pipeline begins once the transcript is available. The raw text is first passed through sentence segmentation, breaking it down into manageable, structured units. Then comes tokenization, where the sentence is split into individual words or phrases. Each token is then analyzed through Part-of-Speech (POS) tagging to determine its grammatical role. Finally, the system performs entity detection, identifying which tokens represent real-world entities, and assigning them a type like person, organization, product, or location.
So when a transcript says:
“Apple just announced the iPhone 15 during their California launch event.”
A trained NER model might return:
[ { "text": "Apple", "type": "Organization" }, { "text": "iPhone 15", "type": "Product" }, { "text": "California", "type": "Location" }]
Suddenly, your transcript is no longer just text. It’s structured data. Now you can tag this video automatically, by product, company, and location. You can make the entire video searchable, extract chapters where each entity appears, or feed this metadata into your analytics dashboard and recommendation engine.
In the next section, we’ll look at why this matters, and how it changes what your video platform is capable of.
Let’s say you’ve got 5,000 hours of video, all transcribed. That’s a good start, but what can you actually do with those transcripts?
Without structure, not much. Your search bar becomes a blunt instrument. Your recommendations stay shallow. Your content tags are vague at best, or missing altogether.
Entity classification fixes that. It turns unstructured transcripts into metadata your platform can act on. If you're building a product with video at the center, here’s where this really shows up:
Search & discovery: Let’s say a user wants to see every video where Elon Musk is mentioned, or every episode recorded in Paris. If your system knows which people, locations, and companies appear in each transcript, that becomes a simple query, not a wishlist feature.
Personalization: Entity metadata makes it easier to recommend videos based on who or what shows up in the content, not just based on categories or past watch history. If someone just watched a lot of videos involving “Marvel,” you can prioritize new content mentioning that entity without relying on tags manually added by a producer.
Compliance & moderation: If you operate in finance, healthcare, politics, or any regulated space, you already know the stakes. Entity classification lets you flag videos where restricted individuals, banned organizations, or sensitive topics are mentioned, automatically, across your entire library.
You don’t need to guess anymore. Once you’ve classified your transcripts with named entities, your video stack gets smarter across the board, search, tagging, chapters, recommendations, monetization, analytics.
Entity recognition is the bridge between raw video and everything else your product wants to do with it.
In the next section, we’ll cover the hard parts because yes, there are a few.
By now, entity classification might sound straightforward. Transcribe the video, run a model, get structured metadata. Done. But in practice, things get messy, fast.
Ambiguity is everywhere: Take the word Apple. Are we talking about the company? The fruit? A location name in a fantasy game? Without context, the machine doesn’t know, and sometimes, even with context, it’s a close call.
Context is everything: “Jordan played in Chicago.”
Is that Michael Jordan, the person? Or is it Jordan, the country, mentioned in passing during a news clip? These decisions can’t be made by looking at one word in isolation. The model has to understand how words relate to each other across the sentence, the paragraph, the entire transcript.
Multilingual transcripts add another layer: Names change pronunciation. Some places have different spellings depending on the language. And the same word can be a person’s name in one region and a brand in another. For global platforms, entity classification quickly turns into a language problem too.
Domain-specific content breaks general-purpose models: If your platform deals with sports, legal commentary, or medical training videos, generic NER models trained on Wikipedia articles won’t cut it. You’ll miss key people, mislabel jargon, or drown in false positives. Customization or fine-tuning becomes critical.
Real-time use cases introduce latency and scale constraints: It’s one thing to tag transcripts in batches. It’s another to classify live captions during a stream, under a few hundred milliseconds, without blowing up your infra costs. Video platforms doing real-time translation or moderation feel this pain constantly.
So yes, entity classification is powerful, but it’s not plug-and-play. It works best when it’s part of a system designed for video, with the quirks of transcripts, speaker changes, domain context, and real-time expectations built in from the start.
In the next section, we’ll show how we’ve approached that problem, and where FastPix fits into the stack.
Entity recognition is only useful if it’s baked into the workflow, not just bolted on after the fact. That’s exactly how it works in FastPix.
When you upload or stream a video through FastPix, the platform automatically handles everything that needs to happen to make your content understandable at scale:
And because it’s part of the core platform, it’s fast, serverless, and doesn’t require you to manage separate models, pipelines, or storage.
Video transcripts on their own aren’t useful, until you add structure.
That’s what FastPix AI does. Our Entity Recognition feature scans transcripts and automatically tags people, places, organizations, and products, turning raw speech into structured metadata you can use across your platform.
Whether you're building smarter search, auto-tagging content, generating chapters, or tracking brand mentions, this AI feature is designed to plug directly into your video pipeline Explore FastPix AI Features to start enriching your videos with entity metadata.
Named entity recognition (NER) is a natural language processing technique that identifies and classifies key entities in text, such as names of people, organizations, locations, and dates.
NER analyzes text by breaking it down into smaller units, tagging these units as specific entity types, and converting unstructured text into structured data for easier analysis.
NER typically identifies categories like individual people (e.g., John Johnson), companies (e.g., Nike), places (e.g., Paris), dates (e.g., January 1, 2023), etc.
Context helps determine the meaning of words that can refer to different entities. For example, "Apple" can mean a fruit or a company, depending on the surrounding text.
Popular NER tools include SpaCy for general tasks, Stanford NER for customization, NLTK for beginners, and Hugging Face Transformers for higher-level deep-learning applications.
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