Image Layout and Content Analysis for Complex Document Understanding
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Solution Overview
Problem
Current image processing technologies have a low ability to understand complex image types found in work scenarios, such as document screenshots, table screenshots, flowcharts, and architecture diagrams, leading to inaccurate answers and poor user experience in human-computer interaction.
Innovation Solution
An image-based human-computer interaction method and apparatus that acquires and analyzes images with multiple modal data types, determining image layout and content information with preset granularity, and generates response information based on user questions using semantic analysis and deep learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current picture understanding technology is used to analyze complex work scenario images, then the processing speed is maintained, but the understanding accuracy is low
Solution Approach 1:
The patent segments the image analysis process into multiple distinct modules: layout analysis module that determines spatial relationships, content analysis module that extracts semantic information, and multi-modal fusion module that integrates different data types. This segmentation allows each module to specialize in specific aspects of image understanding, improving overall accuracy while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent introduces layout information as an additional dimensional layer beyond traditional content analysis. By analyzing both the semantic content and the spatial layout structure of images, the system creates a multi-dimensional understanding framework that captures both what objects are present and how they are arranged, significantly improving understanding accuracy for complex diagrams and documents.
2Adaptability or versatility
If traditional image analysis methods are used, then the system simplicity is maintained, but the ability to understand complex image types is insufficient
Solution Approach 1:
The patent designs a universal multi-modal analysis framework that can handle diverse image types including documents, tables, flowcharts, and architecture diagrams through the same integrated system. The layout analysis module and content analysis module work together to provide unified processing for different image formats, enabling the system to adapt to various complex image types without requiring separate specialized systems.
Solution Approach 2:
The patent introduces layout information as an intermediary representation that bridges the gap between raw image data and semantic understanding. This intermediate layout layer captures spatial relationships and structural information, serving as a mediator that enhances the system's ability to understand complex image types while providing a systematic approach that manages overall system complexity.
3Measurement precision
If detailed layout and content analysis is performed, then the answer accuracy is improved, but the processing time increases
Solution Approach 1:
The patent performs layout analysis as a preliminary step before content analysis and question answering. By pre-processing the image to establish spatial relationships and structural information in advance, the system prepares the data in an optimized format that accelerates subsequent analysis steps. This preliminary action reduces the computational burden during query processing, helping to maintain faster response times despite detailed analysis.
Solution Approach 2:
The patent implements dynamic processing where the depth of analysis is adjusted based on the specific query and image type. The system can adaptively allocate computational resources, performing more detailed layout and content analysis when necessary for accurate answering while using streamlined processing for simpler queries. This dynamic approach balances accuracy requirements with processing time constraints.
Data Source
AI summary
The present disclosure provides an image based human-computer interaction method, which includes: acquiring a to-be-analyzed image, and determining image layout information and image content information of the to-be-analyzed image, where the to-be-analyzed image includes a variety of modal data, the image layout information represents distribution of image elements with preset granularity in the to-be-analyzed image, and the image content information represents a content expressed by the modal data in the to-be-analyzed image; and determining, in response to acquiring question information, response information corresponding to the question information according to the image layout information and the image content information, where the question information represents a question proposed by a user for the to-be-analyzed image, and the response information represents a reply answer corresponding to the question information. By extracting layout information and content information from an image, the accuracy of answering a question and user experience of human-computer interaction are improved.