Image Processing Apparatus Automatic Metadata Generation
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Solution Overview
Problem
Conventional image processing apparatuses require users to manually change settings for identifying character string areas corresponding to key candidates in image data, making it difficult to easily set metadata for different destination systems.
Innovation Solution
An image processing apparatus that identifies key candidates from image data based on defined key types, and sets corresponding value candidates using value type and search area rules, allowing for automatic metadata generation without system-specific setting changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the image processing apparatus uses conventional metadata setting methods with manual configuration for each destination system, then the metadata can be accurately set for specific systems, but the operation complexity increases and ease of use deteriorates
Solution Approach 1:
The image processing apparatus automatically identifies key candidates and value candidates from image data, and sets metadata without requiring manual user configuration. The system performs self-service by autonomously extracting character string areas, determining their semantic meanings, and populating metadata fields according to destination system requirements, thereby resolving the contradiction between metadata accuracy and setting ease.
Solution Approach 2:
The system changes the parameter of metadata setting from manual configuration to automatic extraction. By transforming the setting process from a user-driven parameter input method to an automated parameter extraction method based on image analysis, the system achieves both accurate metadata generation and improved ease of operation.
2Adaptability or versatility
If the image processing apparatus requires system-specific setting changes for different destination systems, then the metadata can be customized for each system, but the device complexity increases
Solution Approach 1:
The image processing apparatus implements a universal metadata setting mechanism that can handle multiple destination systems through a single automated extraction process. The system uses a common framework for identifying key candidates and value candidates that adapts to different destination system requirements without requiring separate configuration procedures, thereby reducing device complexity while maintaining versatility.
Solution Approach 2:
The system segments the metadata generation process into distinct functional modules: image data analysis, key candidate identification, value candidate extraction, and metadata population. This segmentation allows the system to handle different destination systems by configuring parameters at the module level rather than requiring complete system-specific settings, thus reducing overall complexity.
3Ease of operation
If the image processing apparatus manually highlights character string areas for user selection, then the user can control metadata content, but the time required for metadata setting increases
Solution Approach 1:
The image processing apparatus performs self-service by automatically identifying key candidates and value candidates from image data without requiring manual user interaction. The system autonomously analyzes character string areas, determines their semantic meanings, and populates metadata fields, thereby eliminating the time-consuming manual highlighting and selection process while maintaining user control through configurable parameters.
Solution Approach 2:
The system performs preliminary action by pre-identifying key candidates and value candidates from image data before the actual metadata setting is required. This advance preparation allows the metadata to be automatically populated without requiring real-time user intervention, significantly reducing the time required for metadata setting while preserving user control through pre-configurable options.
Data Source
AI summary
An image processing apparatus that enables easy setting of metadata of image data. The image processing apparatus obtains image data associated with a selected work. A key candidate is identified from t image data based on one or more key types defined according to the selected work. A value candidate corresponding to the identified key candidate is identified based on a value type rule and a value search area rule which are defined for each of the one or more key types, and the identified value candidate is set as the metadata of the image data.