Search in Video helps you explore and discover insights across your entire video library by identifying all relevant topics, themes and subject matters. You can search inside videos for words, text overlays, logos, images, and other metadata faster and more accurately. Because of this, you avoid fruitless manual scans of your video library, and save time and money.
Search in Video knows precisely which moment in a video any key topic is mentioned and offers a multilingual "Video Word Search" feature. The platform offers dynamic suggestions of timestamps related to media fragments that make it quicker and easier to unlock the potential of your video content. Your entire video repository can also be thus contextualized and used to gain business insights through sentiment analysis.
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.