Document Page Region Segmentation for Accurate Difference Detection
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Solution Overview
Problem
Current image processing systems fail to accurately detect differences between document data pages, particularly in identifying and isolating common and inherent regions, leading to incomplete or inaccurate differential image generation.
Innovation Solution
An image processing apparatus comprising a common region determination unit, an inherent region extraction unit, and an inherent region connection unit, which determines page common regions and extracts page inherent regions from image data, connecting them to detect differences between old and new image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current image processing systems are used to detect differences between document pages, then the process is simple, but the detection accuracy is low and incomplete
Solution Approach 1:
The patent divides a document page into three distinct regions: common regions (identical across pages), inherent regions (unique to each page), and difference regions (changes between versions). This segmentation enables precise detection by focusing computational resources only on relevant areas, thereby improving detection accuracy without requiring complete system redesign.
Solution Approach 2:
The system performs preliminary classification of page regions before difference detection. By pre-identifying common and inherent regions through image recognition and comparison algorithms, the system prepares the document structure in advance, which streamlines the subsequent difference detection process and maintains operational efficiency.
2Measurement precision
If page regions are not separated into common and inherent regions, then the processing is faster, but the difference detection is inaccurate
Solution Approach 1:
The patent segments the page into common and inherent regions, allowing the system to process each region type with optimized algorithms. Common regions are quickly identified and excluded from detailed comparison, while inherent regions receive focused analysis, thereby maintaining processing speed while improving detection accuracy.
Solution Approach 2:
Different processing strategies are applied to different regions: common regions use rapid matching algorithms for quick identification, while inherent regions use more sophisticated comparison methods. This localized quality approach ensures high accuracy where needed without sacrificing overall processing efficiency.
3Reliability
If all regions of a page are processed uniformly, then the process is simpler, but the identification of common and inherent regions is incomplete
Solution Approach 1:
The patent implements automated segmentation that divides the page into common, inherent, and difference regions using image processing algorithms. This segmentation is performed once and then used throughout the comparison process, ensuring complete region identification without requiring complex manual intervention at each processing stage.
Solution Approach 2:
The system introduces an intermediary classification layer that automatically categorizes regions before detailed comparison. This intermediary step uses pattern recognition and image matching to identify region types, thereby ensuring complete and accurate identification while keeping the overall system architecture manageable through modular design.
Data Source
AI summary
An image processing apparatus includes a common region determination unit, an inherent region extraction unit, and an inherent region connection unit. The common region determination unit determines page common regions which are in common among plural pages of document data. The inherent region extraction unit extracts, as page inherent regions, regions other than the page common regions determined by the common region determination unit from image data in the plural pages. The inherent region connection unit connects plural page inherent regions extracted by the inherent region extraction unit.