Video OCR Engine Using Auxiliary Data for Character Recognition
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Solution Overview
Problem
Conventional Optical Character Recognition (OCR) systems for video data are limited in processing partially blocked text and noise within video frames, failing to leverage extrinsic information for accurate character recognition.
Innovation Solution
A media management system employing video data processing engines that selectively implement video OCR techniques, including video character processing, video cluster processing, and auxiliary data processing, to identify and weight candidate variants based on intrinsic and extrinsic video data properties, and generate adaptation data for improved speech recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional OCR systems process video data using only intrinsic information, then processing speed is maintained, but character recognition accuracy deteriorates due to inability to handle partially blocked text and noise
Solution Approach 1:
The patent merges intrinsic video data (visual frames) with extrinsic data (audio transcripts, metadata, contextual information) to create a comprehensive recognition system. The video OCR engine combines results from multiple data sources, allowing the system to overcome limitations of individual modalities and achieve higher character recognition accuracy even when text is partially blocked or noisy.
Solution Approach 2:
The patent introduces auxiliary processing components as intermediaries between the video data and OCR engine. These include audio transcription modules, metadata extraction modules, and contextual analysis components that prepare extrinsic information for integration with visual data, facilitating accurate character recognition without overwhelming system complexity.
2Measurement precision
If video OCR processes all candidate variants equally, then processing simplicity is maintained, but recognition accuracy deteriorates due to inability to prioritize reliable candidates
Solution Approach 1:
The patent changes the parameter of candidate evaluation by introducing weighting mechanisms. Different candidate variants are assigned different weights based on their reliability indicators such as confidence scores, frequency of occurrence across frames, consistency with audio transcripts, and match with contextual information. This allows the system to prioritize high-quality candidates and improve recognition accuracy without exhaustive processing of all possibilities.
Solution Approach 2:
The patent applies different processing qualities to different candidate variants based on their individual characteristics. High-confidence candidates receive streamlined processing while low-confidence candidates undergo more rigorous verification through multiple data sources. This localized quality adjustment optimizes both accuracy and processing efficiency by allocating computational resources strategically.
3Reliability
If conventional systems use only intrinsic video data for OCR, then data processing simplicity is maintained, but recognition reliability deteriorates in noisy or partially blocked conditions
Solution Approach 1:
The patent creates a multi-functional processing system where a single video input is simultaneously analyzed through multiple pathways: visual OCR, audio transcription, metadata extraction, and contextual analysis. Each component serves multiple purposes - for example, audio transcripts not only provide alternative text sources but also help disambiguate visual recognition results, while metadata provides both contextual information and timing synchronization. This universal approach enhances reliability without proportionally increasing information loss.
Data Source
AI summary
In various embodiments, methods and systems for implementing a media management system, for video data processing and adaptation data generation, are provided. At a high level, a video data processing engine relies on different types of video data properties and additional auxiliary data resources to perform video optical character recognition operations for recognizing characters in video data. In operation, video data is accessed to identify recognized characters. A video OCR operation to perform on the video data for character recognition is determined from video character processing and video auxiliary data processing. Video auxiliary data processing includes processing an auxiliary reference object; the auxiliary reference object is an indirect reference object that is a derived input element used as a factor in determining the recognized characters. The video data is processed based on the video OCR operation and based on processing the video data, at least one recognized character is communicated.