Automatic Content Recognition Using Local OCR Image Analysis
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Solution Overview
Problem
Conventional content recognition solutions require extensive computational resources and server-based processing to identify content being output on user devices, relying on audio/video fingerprints and databases, which is resource-intensive and inefficient.
Innovation Solution
A device-connected system that generates and analyzes images of output content using optical character recognition (OCR) techniques to identify applications and content items, reducing the need for server-based processing and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional content recognition solutions use server-based processing with audio/video fingerprints and databases, then content recognition can be performed, but extensive computational resources and processing time are required
Solution Approach 1:
The user device performs content recognition locally by capturing images and analyzing them using OCR and machine learning models stored on the device itself, eliminating the need to send data to remote servers. This self-service approach reduces computational resource consumption while maintaining recognition accuracy through locally processed analysis of visual content.
Solution Approach 2:
The patent replaces the conventional mechanical system of server-based fingerprint matching with an optical recognition system using images and OCR (Optical Character Recognition). This substitution allows content identification through visual analysis rather than audio/video fingerprint comparison, reducing the computational burden while maintaining or improving recognition capabilities.
2Measurement precision
If conventional solutions use server-based processing to analyze audio/video fingerprints, then content can be identified, but processing time and latency increase
Solution Approach 1:
By implementing self-service content recognition where the user device autonomously captures images, processes them through local OCR and machine learning models, and identifies content without server intervention, the system eliminates network transmission delays and server processing time. This results in significantly reduced latency while maintaining accurate content identification through local analysis.
3Measurement precision
If conventional content recognition relies on databases stored on servers, then content recognition can be performed, but device complexity and network dependency increase
Solution Approach 1:
The patent extracts the content recognition functionality from the server-based infrastructure and implements it directly on the user device. By taking out the dependency on remote databases and processing systems, the solution simplifies the overall system architecture while maintaining robust content recognition capability through locally stored machine learning models and image analysis tools.
Solution Approach 2:
The user device becomes self-sufficient by incorporating all necessary recognition tools, including image capture, OCR processing, and machine learning classification, directly on the device. This self-service approach eliminates network dependencies and reduces system complexity by removing the need for server-based database connections and remote processing services.
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, localized content recognition on user devices, reducing latency and resource consumption while providing accurate viewership statistics and content recommendations.
Implementation Method 1
A device-connected system that generates and analyzes images of output content using optical character recognition (OCR) techniques to identify applications and content items
Data Source
AI summary
Methods, apparatuses, and systems are described for determining content being output by a device. One or more images of content being output on the device may be determined. The one or more images may be analyzed to determine text data displayed in the one or more images. The text data may be used to determine the content being output by the device.