Document Image Mark Removal via Connected Component Clustering
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Solution Overview
Problem
Existing methods for removing marks from document images, such as watermarks and copymarks, are inefficient due to their reliance on pixel-level processing, leading to inaccurate results and degraded reading experiences.
Innovation Solution
A method and apparatus that extract connected components from binary images, cluster them based on grayscale features to find a clustering center, and search for optimal clustering radius and threshold combinations to effectively remove marks by evaluating grayscale features and thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If pixel-level processing is used to remove marks, then the processing method is simple, but the processing accuracy is low and the results are inaccurate
Solution Approach 1:
The patent segments the image processing into multiple levels: binary image processing to extract connected components, grayscale feature analysis to identify mark characteristics, and selective removal based on clustered features. This segmentation allows the system to handle complex mark removal tasks through coordinated simple operations at different processing levels, resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The patent transitions from pixel-level processing to connected component-level processing, adding a structural dimension to the analysis. By extracting and analyzing connected components as discrete entities with specific grayscale features, the system achieves higher accuracy in distinguishing marks from text while maintaining processing efficiency through component-based operations.
2Manufacturing precision
If connected component clustering is used to remove marks, then the mark removal accuracy is improved, but the processing complexity increases
Solution Approach 1:
The patent extracts connected components from the binary image and isolates their grayscale features for separate analysis. By taking out the mark identification task from the overall processing flow and handling it through dedicated feature extraction and clustering steps, the system achieves high accuracy while managing complexity through modular processing stages.
Solution Approach 2:
The patent utilizes grayscale threshold parameters and clustering radius parameters to control the mark removal process. By adjusting these parameters to optimize the separation of mark features from text features, the system achieves high accuracy in mark identification and removal while maintaining manageable processing complexity through parameter-based control.
3Measurement precision
If grayscale threshold clustering is applied, then the distinction between foreground text and background noise is improved, but the computational time increases
Solution Approach 1:
The patent performs preliminary processing by extracting connected components and computing their grayscale features before the actual mark removal. This preliminary action organizes the data in advance, enabling more efficient subsequent processing and reducing overall computational time while maintaining high precision in distinguishing foreground text from background noise.
Data Source
AI summary
A method for removing a mark in a document image includes: extracting connected components from a binary image corresponding to the document image; clustering the connected components based on grayscale features of the connected components to obtain one clustering center; searching, within numerical ranges of a clustering radius R and a grayscale threshold T, for a combination (R, T) which causes an evaluation value based on the grayscale features of the connected components to be higher than a first evaluation threshold; and removing the mark in the document image based on the grayscale threshold in the combination. The method and an apparatus according to the invention can remove a mark in a document image effectively and accurately.