Search in video finds that exact moment you're looking for in your video content

Step 1. Converting speech to text

The tool converts videos to text through the use of speech-to-text models. These models are unique to each language and are built using neural networks

Step 2. Indexing the text

The model indexes the text section by section. Videos are broken into sections based on different parameters such as pauses or change in speakers. By analyzing the content section-wise, we get enough context to disambiguate entities. This also helps in yielding timestamps for each keyword or defined entity.

Step 3. Extracting matching results

Now that we have timestamps of videos associated with entities, themes, and topics found in the text, when deep search is applied, it yields all matching sections or snippets in the source video.

Named Entity Recognition

Video content analysis uses named entity recognition (NER) to highlight the entities found at each timestamp, allowing you to scrub and search for the video at the exact point you want. Repustate's semantic search for video is supported in 27 languages and dialects. It can address videos from all sources including social media listening, corporate knowledge management repositories, and online video libraries.