Document Image Binarization via Multi-Threshold Connected Component Analysis
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Solution Overview
Problem
Conventional binarization methods struggle with accurately removing color backgrounds and embedded images from document images, often requiring high computational costs and being inefficient due to the use of global or local thresholding techniques that are not effective for images with varying quality.
Innovation Solution
A method that binarizes document images multiple times using different thresholds, applies connected component analysis to identify and remove noise, and selects an optimum binary image based on statistical analysis of content and noise components across multiple threshold levels, allowing for efficient removal of color backgrounds and embedded images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If global binarization threshold methods (e.g., Otsu's method) are used, then the binarization process is simple and fast, but the method fails to accurately handle document images with color backgrounds and embedded images
Solution Approach 1:
The patent segments the binarization process into multiple iterations, each using a different threshold value. Instead of using a single global threshold, the method divides the threshold selection into discrete levels and evaluates each separately, allowing accurate identification of the optimal threshold even in complex images with color backgrounds and embedded images.
Solution Approach 2:
The patent employs dynamic threshold adjustment by iteratively testing multiple threshold values and selecting the one that produces the best binarization result. The threshold is not fixed but dynamically determined through statistical analysis of connected components at different threshold levels, enabling adaptive handling of various image types.
2Adaptability or versatility
If local threshold binarization methods are used to handle low quality camera images, then the method can deal with varying image quality, but the computational cost becomes very high
Solution Approach 1:
The patent applies local quality analysis by examining the statistical properties of connected components at different spatial locations and threshold levels. It evaluates metrics such as connected component size distribution, fill rate, and spatial patterns to determine the optimal threshold locally, enabling adaptation to varying image quality without requiring computationally intensive local thresholding at every pixel.
Solution Approach 2:
The patent changes the threshold parameter across multiple discrete levels and evaluates the binarization results using statistical metrics. By systematically varying the threshold parameter and selecting the optimal value based on connected component analysis, the method achieves image quality adaptability while maintaining reasonable computational complexity through efficient parameter search.
3Measurement precision
If multiple binarization thresholds are tested to improve threshold determination accuracy, then the accuracy of removing color backgrounds and embedded images improves, but the computational complexity increases
Solution Approach 1:
The patent implements feedback by evaluating the binarization results at each threshold level using statistical metrics (connected component size, fill rate, spatial distribution) and using this feedback to select the optimal threshold. The feedback loop compares different threshold results and automatically identifies the best one, reducing the need for manual intervention while maintaining high accuracy.
Solution Approach 2:
The patent replaces complex manual threshold selection mechanisms with automated statistical analysis. Instead of requiring manual adjustment or complex adaptive algorithms, the system uses computational statistics on connected components to automatically determine the optimal threshold, simplifying the overall process while maintaining high accuracy through objective metric-based selection.
Data Source
AI summary
A method for binarization of document image using multi-threshold process to determine an optimum global binarization threshold for the image. The optimum binarization threshold is determined by binarizing the document multiple times using different threshold values, and calculating the statistics of the useful information and noise for each threshold value to select the optimum threshold value.