Video Text Extraction via Frame Segmentation and Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in accurately extracting and processing textual elements from video content due to the time-consuming and costly analysis of frames, as well as the inability of current OCR systems to recognize text in noisy backgrounds, leading to inaccurate results and inefficiencies in previewing video content.
Innovation Solution
A system that selectively analyzes a portion of video content, filters frames to remove non-textual elements, and uses OCR to identify textual elements, allowing for efficient extraction and storage of relevant information such as production credits and product names, enabling precise skipping of unwanted segments during previews.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If each frame of video is analyzed to extract text, then text extraction accuracy is improved, but processing time and computational cost increase significantly
Solution Approach 1:
The patent segments the video content into specific time intervals (e.g., first 10% of video duration) where text elements are most likely to appear, such as opening credits and closing credits. Instead of analyzing every frame throughout the entire video, the system focuses computational resources on these segmented portions, reducing processing time while maintaining extraction accuracy for the most relevant text content.
Solution Approach 2:
The patent employs preliminary actions by detecting and removing title cards and introductory sequences before the main video content begins. This preliminary processing identifies and excludes segments that contain minimal or no text information, allowing the system to skip direct frame analysis for these portions and thereby reducing overall computational time while preserving accuracy for meaningful text extraction.
2Extent of automation
If current OCR systems are used to recognize text in video frames, then text identification is automated, but accuracy deteriorates due to noisy backgrounds and insufficient contrast
Solution Approach 1:
The patent extracts and removes title cards and introductory sequences from the video content before text extraction. By taking out these segments that contain minimal text and potentially misleading visual information, the system reduces the complexity of the remaining content, thereby improving OCR accuracy for the text that does remain in the analyzed portions.
Solution Approach 2:
The patent applies different processing quality levels to different portions of the video. High-quality frame-by-frame analysis with advanced OCR is applied only to segments where text is expected (opening credits, closing credits, intertitle cards), while lower-quality or skipped processing is applied to segments where text is unlikely to appear. This local quality differentiation maintains high accuracy where needed while reducing overall computational burden.
Data Source
AI summary
Video content comprising a plurality of frames containing textual and non-textual elements is processed. A portion of the plurality of frames is selected for analysis to identify textual elements in the frames corresponding to pre-defined textual elements. The identified textual elements are stored along with their location within the video content. In some embodiments, each of a subset of frames included in the portion is analyzed until the pre-defined textual element is identified in a start frame. A plurality of successive frames subsequent to the start frame is analyzed to identify pre-defined textual elements in the frame. Analyzing the frames includes filtering the frames to remove non-textual elements and increase the visibility of the textual elements contained therein. A confidence rating is calculated for the identified textual elements according to some embodiments.