Document Image Segmentation for Accurate Background Removal
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Solution Overview
Problem
When paper-based documents are digitized using digital cameras, the captured background often complicates further processing such as optical character recognition.
Innovation Solution
A method and system for background removal that includes pixel clustering, foreground/background classification, and cropping to isolate the document content from the background, utilizing color space conversions and clustering algorithms like k-means.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If background is captured along with document during digitization, then complete scene is recorded, but processing accuracy deteriorates due to background interference
Solution Approach 1:
The patent applies segmentation by dividing the captured image into multiple segments using k-means clustering algorithm. The image is partitioned into foreground segments (containing document content) and background segments, allowing selective processing of only the relevant document portions while eliminating background interference from OCR processing.
Solution Approach 2:
The patent extracts the foreground document content from the captured image by identifying and isolating pixels belonging to the document versus background. This extraction process removes the harmful background elements while preserving the essential document content for accurate optical character recognition.
2Measurement precision
If simple cropping is used to remove background, then processing speed is maintained, but document boundary detection accuracy deteriorates
Solution Approach 1:
The patent performs preliminary clustering and classification operations before the actual cropping step. By pre-identifying foreground and background regions through k-means clustering and pixel classification, the system determines precise document boundaries in advance, enabling accurate cropping without requiring complex real-time analysis during the cutting phase.
Solution Approach 2:
The patent replaces simple geometric or threshold-based cropping methods with a data-driven approach using machine learning algorithms (k-means clustering). This substitution allows the system to intelligently identify document boundaries based on pixel characteristics rather than relying on fixed mechanical rules, achieving higher accuracy while maintaining computational efficiency.
Data Source
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AI summary
The invention relates to a method for background removal from documents. The method includes obtaining an image of a document, performing a clustering operation on the image to obtain a plurality of image segments, and performing, for each image segment, a foreground/background classification to determine whether the image segment includes foreground. The method further includes obtaining an augmented image by combining the image segments that include foreground, and obtaining a background-treated image by cropping the image of the document, based on the foreground in the augmented image.