Multimedia Context Keyword Generation via ASR and OCR Convergence
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Solution Overview
Problem
Current methods for identifying viewer intent and context in multimedia content are manual, tedious, and inadequate for the scale of online video content, failing to automatically generate context-dependent keywords and keyphrases that effectively summarize important concepts.
Innovation Solution
The use of Automatic Speech Recognition (ASR), Optical Character Recognition (OCR), Computer Vision (CV), machine learning (ML), and Natural Language Processing (NLP) to automatically generate context-dependent keywords and keyphrases from multimedia content, combining audio and visual analysis to capture the essence of the media.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to create video indexes and transcripts, then the quality of viewer context information can be maintained, but the productivity and scalability are severely limited
Solution Approach 1:
The system enables multimedia content to automatically generate its own context information through self-service mechanisms. ASR automatically transcribes speech, OCR automatically extracts text from visual elements, and CV automatically identifies objects and scenes, eliminating the need for manual annotation while maintaining high accuracy through multiple independent analysis pathways converging on the same content.
Solution Approach 2:
The video content processing is divided into multiple independent segmentation tasks: speech-to-text conversion, text extraction from visual elements, object recognition, and scene analysis. Each segmentation is handled by specialized algorithms that can process different portions of the content simultaneously, dramatically increasing overall productivity while maintaining precision through focused specialization.
2Productivity
If automated methods are used to generate video indexes and transcripts, then the productivity and scalability are improved, but the accuracy and quality of viewer context information deteriorate
Solution Approach 1:
Multiple automated analysis methods are merged into a unified system where ASR, OCR, and CV results are combined and cross-validated. The system merges transcript data, extracted text, object labels, and scene descriptions into a cohesive context model, using the convergence of multiple independent analyses to verify and enhance accuracy while maintaining high processing speed.
Solution Approach 2:
The system introduces intermediary processing layers that mediate between raw automated analysis outputs and final context information. These intermediaries include normalization modules, disambiguation algorithms, and context-aware filtering mechanisms that refine the outputs of ASR, OCR, and CV systems, ensuring high accuracy without sacrificing the automated processing speed.
3Loss of information
If comprehensive video analysis is performed to capture all important concepts, then the viewer's viewing intent can be fully understood, but the time required to process and present the information increases
Solution Approach 1:
The system extracts only the most salient and relevant information from the comprehensive video analysis. Rather than presenting all detected objects, scenes, and concepts, the system identifies and extracts key themes, important entities, and critical information points, discarding redundant or less significant data to provide a concise summary that captures essential concepts without excessive processing time.
Solution Approach 2:
The system performs preliminary analysis and prioritization of video content elements before final processing. By pre-identifying key themes, important segments, and high-value information during initial scanning, the system can focus detailed analysis only on these prioritized elements, capturing all important concepts while minimizing time spent on less critical content.
Data Source
AI summary
Embodiments herein disclose methods and systems for automatic generation and convergence of keywords and/or keyphrases from a media. A method disclosed herein includes analyzing at least one source of the media to obtain at least one text, wherein the at least one source includes at least one audio portion and at least one visual portion. The method further includes extracting at least one keyword of a plurality of keywords from the extracted at least one text. The method further includes generating at least one keyphrase of a plurality of keyphrases for the extracted at least one keyword. The method further includes merging at least one of the at least one keyword and the at least one keyphrase to generate a plurality of elements from the media, wherein the plurality of elements includes context dependent set of at least one of the plurality of keywords and the plurality of keyphrases.