Document Boundary Detection Using Kernel Transformations
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Solution Overview
Problem
Existing image processing techniques struggle to accurately detect the boundary of a document within a digital image, especially when the image is captured in real-time during transactions, due to occlusions, irregular edges, and poor contrast between the document and background.
Innovation Solution
A document detection system using kernel-based transformations and quadrilateral scoring, which converts the image to a binary format, applies morphological transformations with horizontal and vertical kernels, and probabilistic Hough line transformations to identify and refine the document boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional edge detection and contour detection techniques are used, then the processing speed is fast, but the detection accuracy deteriorates when the document boundary is occluded or has irregular edges
Solution Approach 1:
The patent segments the document boundary detection problem into multiple stages: initial contour detection to identify candidate regions, occlusion analysis to detect foreground objects blocking the boundary, and iterative refinement to reconstruct occluded edges. This segmentation allows the system to handle complex occlusion cases without requiring a completely different approach for each scenario.
Solution Approach 2:
The patent performs preliminary actions by first detecting the overall document contour before attempting to identify specific boundary features. It also performs preliminary occlusion detection by analyzing foreground objects before final boundary reconstruction. This preliminary analysis prepares the system to handle occlusions more effectively during the final boundary detection stage.
2Measurement precision
If the system waits for complete document boundary detection before processing, then detection accuracy improves, but the processing time increases
Solution Approach 1:
The patent applies partial action by detecting and processing the non-occluded portions of the document boundary first, rather than waiting for complete boundary detection. It identifies visible edges and contours immediately and processes them, while simultaneously working to reconstruct occluded portions. This allows the system to make progress on document processing without waiting for perfect boundary detection of all regions.
Solution Approach 2:
The patent implements feedback mechanisms where the detected boundary information is continuously refined based on occlusion analysis results. The system uses feedback from initial contour detection to guide occlusion analysis, and then uses feedback from occlusion analysis to refine the final boundary reconstruction. This iterative feedback process improves accuracy while maintaining efficient processing through targeted refinements rather than complete re-detection.
3Adaptability or versatility
If the document boundary requires straight edges and 90 degree corners for detection, then the detection process is simple, but the system cannot handle worn or irregular documents
Solution Approach 1:
The patent changes the geometric parameters used for boundary detection from strict straight lines and 90-degree angles to more flexible representations. It allows for curved edges, varying angles, and irregular shapes by adapting the detection algorithms to work with general contour representations rather than requiring specific geometric constraints. This parameter flexibility enables the system to handle worn, folded, or irregularly shaped documents.
Solution Approach 2:
The patent introduces dynamics by making the boundary detection process adaptive rather than static. The system dynamically adjusts its detection criteria based on the actual document characteristics observed in the image. Instead of requiring all documents to conform to fixed geometric rules, the detection parameters and methods are dynamically modified to match the specific document being analyzed, enabling handling of diverse document types and conditions.
Data Source
AI summary
Methods and systems are presented for detecting a boundary of a document within a digital image. Upon receiving an image, the image is converted into a binary image. One or more kernel-based transformations are performed on the binary image using a horizontal kernel and a vertical kernel. A plurality of edges are identified based on the one or more kernel-based transformations. The plurality of edges includes a plurality of horizontal edges and a plurality of vertical edges. Multiple quadrilaterals are constructed using different combinations of horizontal edges and vertical edges from the plurality of edges. A particular quadrilateral is selected from the multiple quadrilaterals based on how well the edges fit the perimeters of the quadrilaterals. The selected quadrilateral is used to define a boundary of the document within the digital image.