Visual Mode Image Comparison for Document Consistency
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Solution Overview
Problem
Conventional methods for comparing images, such as scanned documents, are inefficient and prone to human error, and automated approaches like OCR often require excessive resource consumption and fail to accurately detect inconsistencies in non-character regions.
Innovation Solution
A method that performs a visual classification analysis to identify overlapping regions between images, followed by a character recognition analysis on specific areas to verify content consistency, using affine image processing and machine learning models for accurate and efficient comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated character recognition analysis is performed on entire images, then productivity is improved, but use of energy increases and measurement precision deteriorates due to false positives and negatives
Solution Approach 1:
The patent divides the image comparison process into two distinct stages: visual classification analysis that processes entire images to identify candidate regions, and character recognition analysis that processes only those specific candidate regions. This segmentation allows the computationally intensive character recognition to be applied selectively rather than uniformly across all images, reducing overall resource consumption while maintaining accuracy.
Solution Approach 2:
The patent applies different analysis methods to different regions of images based on their characteristics. Visual classification is applied globally to identify regions of interest, while character recognition is applied locally only to those identified regions. This local quality approach ensures that computational resources are concentrated where they are most needed, improving both efficiency and precision.
2Measurement precision
If visual classification analysis is performed on entire images, then measurement precision is improved for identifying inconsistent regions, but use of energy increases
Solution Approach 1:
The patent performs visual classification analysis as a preliminary step before character recognition analysis. This preliminary action identifies and flags only those regions that are likely to contain inconsistencies, preparing a reduced set of candidate regions for the more computationally intensive character recognition process. This preliminary filtering action significantly reduces the energy required for subsequent detailed analysis.
3Measurement precision
If multi-tiered analysis approach is used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the analysis system into two distinct modules: a visual classification analysis module and a character recognition analysis module. Each module has a specific function and processes data in a structured sequence. This segmentation, while adding functional complexity, organizes the system in a way that makes each component manageable and well-defined, with clear interfaces between them.
Solution Approach 2:
The patent introduces visual classification analysis as an intermediary step between image input and character recognition analysis. This intermediary process filters and identifies candidate regions, serving as a bridge that connects the raw image data to the detailed character recognition process. This intermediary layer simplifies the overall system architecture by creating a clear pipeline with defined stages and reducing the complexity of direct full-image character recognition.
Data Source
AI summary
A method, a computer program product, and a computer system compare images for content consistency. The method includes receiving a first image including a first document and a second image including a second document. The method includes performing a visual classification analysis on the first image and the second image. The visual classification analysis generates an overlap of the first image with the second image. The method includes determining whether a region of the overlap is indicative of a content inconsistency. As a result of the region of the overlap being indicative of a content inconsistency, the method includes performing a character recognition analysis on a first area of the first image and a second area of the second image corresponding to the region of the overlap to verify the content inconsistency.