Document Image Boundary Detection Using Connected Component Analysis
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Solution Overview
Problem
Existing image cropping methods struggle to accurately distinguish between black borders and black areas within documents, especially when contact image sensors produce defects like speckles and white stripes, making it difficult to correctly adjust scanned document images.
Innovation Solution
A method that identifies the largest connected component in a digital document image and determines its boundaries, using a combination of connected component analysis and row-by-row and column-by-column analysis to eliminate defects and assign accurate boundaries, with optional parallel processing to select the best boundary approximation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If ordinary cropping methods are used to remove black borders, then the black border is eliminated, but document content areas with black color are incorrectly removed as well
Solution Approach 1:
The patent segments the image processing into multiple distinct steps: connected component analysis to identify document regions, defect detection to identify CIS defects, and boundary determination to establish crop boundaries. This segmentation allows the system to distinguish between document content and borders by analyzing different image characteristics at different processing stages, preventing loss of black document content while removing black borders.
Solution Approach 2:
The patent applies different processing strategies to different regions of the image. Connected component analysis is applied to identify document areas, while row-by-row and column-by-column analysis are applied specifically to detect CIS defects. The boundary determination uses confidence ranges and multiple analysis methods tailored to specific image characteristics, allowing precise local discrimination between document content and borders.
2Measurement precision
If connected component analysis is used to identify document boundaries, then document regions are identified, but CIS defects like speckles and white stripes are misidentified as document content
Solution Approach 1:
The patent introduces an intermediary defect detection step between connected component analysis and boundary determination. This intermediary analysis specifically identifies CIS defects (speckles, white stripes, edge defects) using row-by-row and column-by-column analysis patterns. By inserting this intermediary layer, the system can filter out false positives from connected component analysis before final boundary determination, improving both precision and reliability.
Solution Approach 2:
The patent employs multiple overlapping analysis methods (connected component analysis, row-by-row analysis, column-by-column analysis) that collectively provide more detection coverage than any single method. This excessive action ensures that CIS defects are detected through multiple independent analysis paths, increasing reliability by requiring consensus among different detection methods before identifying a region as a defect.
3Manufacturing precision
If multiple analysis methods are used to improve boundary accuracy, then cropping precision is improved, but processing complexity increases
Solution Approach 1:
The patent implements a dynamic, adaptive processing system that adjusts the complexity of analysis based on image characteristics. The system uses confidence ranges to determine when additional analysis is necessary and when simpler methods suffice. This dynamic approach allows the system to maintain high cropping precision while avoiding unnecessary processing complexity for images that are easily identifiable, optimizing the balance between accuracy and computational resources.
Solution Approach 2:
The patent incorporates feedback mechanisms where the results of connected component analysis inform the defect detection process, and the results of defect detection refine the boundary determination. The system uses confidence ranges as feedback to determine when additional analysis is needed and adjusts processing accordingly. This feedback loop allows the system to achieve high precision by applying complex analysis only when necessary, rather than always using the most complex methods.
Data Source
AI summary
The boundaries of a scanned digital document are determined by identifying the largest connected component in the received digital document and assigning the boundaries of the largest connected component as the boundaries of the received digital document or by using a row by row and column by column analysis of the received digital document to identify horizontal and vertical bands in the digital image having pixels with a value opposite to the value of pixels of a background of the received digital document and assigning the horizontal and vertical bands to be the boundaries of the received digital document. These processes may be performed in series or parallel by a processor associated with a scanner that creates the digital document.