Video to Text API

Convert any video into structured, machine-readable text. Not just transcription — VidContext captures speech, visual scenes, on-screen text, brands, and audio in a single JSON response. One API call replaces an entire multimodal processing pipeline.

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More than transcription

Most video-to-text tools only capture speech. But a video communicates through far more than words: visual context, text overlays, brand placements, music choices, and scene composition all carry meaning. Losing any of these layers means losing information.

VidContext converts every layer into structured text. The speech becomes a timestamped transcript. The visuals become scene descriptions. On-screen text is extracted verbatim. Brands and logos are identified. Audio is classified. The result is a complete text document that represents everything a human viewer would perceive — ready for AI consumption, search indexing, or downstream analysis.

Three layers of text extraction

Speech to text

Timestamped transcript of all spoken content with speaker context and dialogue segmentation.

Visual to text

Natural-language descriptions of every scene — who appears, what they do, the environment, camera angles, transitions.

Screen text extraction

Verbatim capture of all on-screen text: titles, lower thirds, URLs, product names, watermarks, subtitles.

Brand identification

Logos, products, and brand mentions detected across visual and audio channels, listed with timestamps.

Audio classification

Music genre, sound effects, ambient noise, and overall audio mood converted to descriptive text labels.

Structured JSON output

Every text layer organized in clean, labeled JSON sections with consistent timestamps for easy parsing.

Send a video, get text back

A 3-minute video returns a complete text representation in about 50 seconds. Every section is clearly labeled for parsing.

curl -X POST https://api.vidcontext.com/v1/analyze \
  -H "X-API-Key: vc_your_key" \
  -F "source=https://example.com/tutorial.mp4" \
  -F "output_format=context"
{
  "transcript": [
    { "time": "0:00-0:12", "text": "Today we are going to walk through..." },
    { "time": "0:12-0:30", "text": "First, open the settings panel..." }
  ],
  "visual_scenes": [
    {
      "time": "0:00-0:08",
      "description": "Close-up of presenter, office background, webcam framing"
    },
    {
      "time": "0:08-0:25",
      "description": "Screen recording: settings panel open, cursor navigating menu"
    }
  ],
  "on_screen_text": ["Settings > Preferences > API Keys", "Step 1: Generate Key"],
  "brands_detected": ["Chrome", "VS Code"],
  "audio": { "music": "none", "effects": ["mouse clicks", "keyboard typing"] }
}

What developers build with video-to-text

  • RAG over video libraries — Convert video archives into searchable text, then use retrieval-augmented generation to answer questions about video content without re-watching.
  • Accessibility pipelines — Generate comprehensive descriptions that go beyond basic captions, including visual context for viewers who cannot see the video.
  • Content repurposing — Transform video content into blog posts, documentation, social media copy, or training materials using the full text output as source material.
  • Compliance and archival — Create searchable text records of video content for regulatory compliance, legal discovery, or institutional archives.

Frequently asked questions

What text do you extract from video?

VidContext extracts three layers of text: speech (full timestamped transcript), visual text (anything displayed on screen like titles, captions, URLs, and watermarks), and descriptive text (natural-language descriptions of every visual scene, including people, objects, settings, and actions).

Is this just transcription?

No. Transcription only captures speech. VidContext converts everything in the video to text — what was said, what was shown, what text appeared on screen, what brands were visible, what music was playing, and what the overall visual environment looked like. The result is a complete text representation of the entire video.

What about videos without speech?

VidContext works just as well on silent videos, music videos, and videos with no dialogue. Visual scene descriptions, on-screen text extraction, brand detection, and audio analysis (music, sound effects) all work independently of speech content.

What output format do I get?

All output is structured JSON with clearly labeled sections: metadata, transcript, visual_scenes, on_screen_text, brands_detected, and audio. Each section uses consistent formatting with timestamps, making it easy to parse programmatically or feed to another AI model.

Can I feed this output to another AI model?

Yes. The structured text output is designed to be consumed by LLMs, AI agents, and automation workflows. Many users pipe VidContext output directly into GPT-4, Claude, or custom models for further analysis, summarization, or decision-making. The MCP server (pip install vidcontext-mcp) makes this seamless for AI agents.

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