AI Document Style Transfer for Fast Design-Preserving Conversion
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Solution Overview
Problem
Existing document analysis and recognition technologies struggle to preserve design features when converting paper documents to digital format, requiring specialized tools and taking extended time for styling adjustments.
Innovation Solution
A method and electronic device that utilize AI and machine learning to extract document style information from a reference document, allowing for quick and variable adjustments to the style of a target document based on external input signals, using techniques like neural networks, clustering algorithms, and geometric approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If document conversion is performed using traditional DAR technology, then information extraction is achieved, but design features are lost and styling requires specialized tools and extended time
Solution Approach 1:
The patent segments document style into multiple independent components including layout structure, typography characteristics, color schemes, and formatting elements. This segmentation allows each design feature to be extracted and preserved independently during conversion, preventing information loss while maintaining overall document aesthetics.
Solution Approach 2:
The system performs preliminary extraction and analysis of design features from the source document before the actual conversion process. By pre-identifying and storing style characteristics in a structured format, the system prepares all necessary styling information in advance, enabling rapid application during conversion without requiring specialized post-processing tools.
2Manufacturing precision
If specialized styling tools are used to preserve design features, then design quality is maintained, but processing time is extended
Solution Approach 1:
The patent replaces traditional mechanical styling tools and manual adjustment processes with an AI-based machine learning system. The neural network automatically analyzes design features and applies styling transformations through computational algorithms, achieving precision comparable to specialized tools but at significantly reduced processing speeds and without requiring expert intervention.
Solution Approach 2:
The system transforms styling from a complex manual adjustment process into a set of可调 parameters that can be modified through simple user inputs. By representing document style as adjustable parameters (font sizes, colors, spacing, layout configurations), the system enables precise style control through parameter optimization rather than manual formatting operations.
3Adaptability or versatility
If multiple style adjustments are performed with different adjustment levels, then customization flexibility is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal style adjustment framework that handles multiple adjustment levels and customization requirements through a single integrated system. The machine learning model is designed to process various types of style modifications (minor tweaks, major transformations, selective adjustments) using the same underlying architecture, eliminating the need for separate tools for each adjustment type and simplifying the user interface.
Data Source
AI summary
A method of adjusting a document style includes obtaining a target document from a user, obtaining a style reference document; extracting, from the style reference document, document style information representing a document style of the style reference document, adjusting a document style of the target document, based on the document style information and a first external input signal indicating a first document style adjustment level of the target document, and displaying the adjusted target document with the adjusted document style.