Resolution-Based Text Extraction for Searchable Communication Videos
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Solution Overview
Problem
Existing digital communication platforms lack the ability to automatically extract textual content from video recordings of communication sessions, making it difficult and time-consuming to analyze and search for specific information in sales meetings and other video content.
Innovation Solution
A system that extracts frames from video content, classifies them based on image analysis, identifies frames containing text, detects titles, and uses optical character recognition (OCR) to extract textual content, transmitting it to client devices for analysis and search functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual scrolling and searching through video recordings is used, then no additional processing is needed, but the time required to analyze and search for specific information increases significantly
Solution Approach 1:
The system performs preliminary extraction of textual content from video frames during or after the communication session, creating a searchable text index before the user needs to search. This allows users to immediately search extracted text without manually watching video content, resolving the contradiction between retrieval efficiency and time investment.
Solution Approach 2:
The patent introduces an intermediary text layer between the video content and the user search function. Instead of directly searching video pixels or manually reviewing content, the system creates a text-based intermediary representation that can be searched efficiently while still representing the underlying video information.
2Loss of information
If OCR is applied to all video frames, then complete textual content is extracted, but the processing time and computational resources increase
Solution Approach 1:
The system extracts and processes only the relevant portion of video content by identifying frames that contain textual information and applying OCR selectively to those frames. This extraction approach maintains completeness of textual content while avoiding unnecessary processing of frames without text, thus preserving processing speed.
Solution Approach 2:
The patent applies partial action by performing OCR on only those video frames that are likely to contain textual content, rather than processing every frame. This selective approach uses sufficient processing to capture all relevant text while avoiding the excessive processing that would result from analyzing every frame.
3Measurement precision
If high-resolution frames are used for OCR, then text extraction accuracy improves, but the processing load and time increase
Solution Approach 1:
The system applies local quality by using high-resolution processing only where needed (in frames containing text) rather than uniformly across all video content. This allows the patent to maintain high text extraction accuracy for relevant frames while reducing overall processing complexity by using lower resolution or no processing for frames without text.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient extraction and transmission of textual content from video frames, improving the ability to analyze and search video recordings of communication sessions, enhancing sales performance by reducing the time spent on manual scrolling and searching.
Implementation Method 1
extracting, via optical character recognition (OCR), the title from the cropped title area of the high-resolution version of the frame
Data Source
AI summary
Methods and systems provide for resolution-based extraction of textual content. In one embodiment, the system receives video content of a communication session with participants. The system then extracts high-resolution versions and low-resolution versions of frames from the video content, and classifies the low-resolution frames of the video content based on identifying text within the low-resolution frames. The system identifies one or more low-resolution distinguishing frames containing text. For each low-resolution distinguishing frame containing text, the system detects a title within the frame, crops a title area with the title within the frame, and extracts, via optical character recognition (“OCR”), the title from the cropped title area of the high-resolution version of the frame. The system extracts, via OCR, textual content from the high-resolution versions of the low-resolution distinguishing frames containing text, and then transmits the extracted textual content and extracted titles to one or more client devices.