Video Tagging via OCR Text Extraction and Segment Propagation
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Solution Overview
Problem
Existing video search technologies rely heavily on human-tagged metadata, leading to a vast amount of unsearchable content as only content with sufficient human interest or commercial value gets tagged, and tags are often applied at a coarse level, making it difficult to find specific segments within videos.
Innovation Solution
Automating the tagging process by analyzing videos to identify segments through color histogram analysis and optical character recognition (OCR) to extract text, allowing for detailed tagging of video segments, and propagating tags to similar segments based on histogram similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If human tagging is used to apply tags to video content, then tags can be applied to content that interests users or has commercial value, but the vast majority of video content remains untagged and unsearchable
Solution Approach 1:
The video content itself provides the tagging information through text that appears within the video frames. The system extracts text directly from the video content, eliminating the need for external human tagging. This self-service approach allows any video with visible text to be automatically tagged and made searchable without requiring human intervention.
Solution Approach 2:
The manual human tagging process is replaced with automated optical character recognition (OCR) technology. The system uses computer vision algorithms to detect and extract text from video frames, converting visual text information into searchable metadata automatically. This mechanical substitution enables scalable processing of large volumes of video content.
2Ease of operation
If tags are applied to entire videos by humans, then tagging effort is reduced, but tags are applied at a coarse level and cannot identify specific segments within videos
Solution Approach 1:
The system divides the video into temporal segments based on when specific text appears in the video frames. Each text extraction event creates a segmented time range, allowing precise identification of video portions containing relevant information. This segmentation enables both automated processing and fine-grained tagging without requiring manual effort.
Solution Approach 2:
The system performs text extraction and segment identification automatically during video processing, before any search operation occurs. By pre-segmenting the video based on text appearance and creating searchable metadata in advance, the system enables efficient segment-level searching without requiring manual tagging effort at the time of search.
3Loss of information
If automated text extraction is performed on every video frame, then all text information can be captured, but the processing time and computational resources increase significantly
Solution Approach 1:
Instead of processing every single frame, the system strategically samples frames at key moments when text is likely to appear or change. This partial action approach captures sufficient text information for effective tagging while significantly reducing the total number of frames requiring OCR processing, thereby balancing completeness with processing efficiency.
Solution Approach 2:
The video processing is divided into segments based on detected text appearance events. The system identifies temporal boundaries where text appears or disappears and processes frames within those boundaries, rather than uniformly processing all frames. This segmentation reduces overall processing time while maintaining complete text information capture.
Data Source
AI summary
In one example, video may be analyzed and divided into segments. Character recognition may be performed on the segments to determine what text appears in the segments. The text may be used to assign tags to the video and/or to the segments. Segments that appear visually similar to each other (e.g., segments that appear to be different views of the same person) may be grouped together, and a tag that is assigned to one segment may be propagated to another segment. The tags may be used to perform various types of tasks with respect to the video. One example of such a task is to perform a search on the video.