Image Processing Apparatus for Blurred Character Extraction and Noise Suppression
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Solution Overview
Problem
Existing image processing techniques fail to appropriately specify groups of black pixels for processing within binary images, leading to potential misidentification of characters and noise regions.
Innovation Solution
An image processing method that involves binarization processing with adjustable thresholds to differentiate between character and noise regions, using projective transformations for distortion correction and shadow removal, and labeling adjacent black pixels to determine marker attributes for monochromatization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a binarization threshold is lowered to convert blurred character parts into black pixels, then character completeness is improved, but noise regions are also converted into black pixels increasing false positives
Solution Approach 1:
The patent segments the binary image into multiple candidate groups and evaluates each group independently using multiple criteria (area ratio, density, shape factors). This segmentation allows differentiated treatment of character regions versus noise regions, enabling the system to select valid character groups while rejecting noise even when both appear as black pixels in the binary image.
Solution Approach 2:
The patent employs multiple binarization thresholds to generate multiple binary images with different parameter settings. By evaluating candidate groups across different threshold conditions and selecting groups that consistently meet validation criteria, the system adapts to varying image conditions while maintaining accurate character detection and noise suppression.
2Measurement precision
If multiple binary images are generated with different thresholds, then character detection coverage is improved, but processing complexity increases
Solution Approach 1:
The patent performs preliminary binarization processing to generate multiple binary images with different thresholds before the main character detection phase. This preliminary action creates a foundation of candidate groups that can be systematically evaluated, reducing the complexity of subsequent detection by pre-organizing potential character regions across multiple threshold conditions.
Solution Approach 2:
The patent merges the results from multiple binary images by evaluating candidate groups across all threshold conditions and selecting groups that satisfy validation criteria. This merging approach consolidates the detection coverage from multiple thresholds while maintaining a unified decision-making process through consistent evaluation standards.
3Productivity
If all black pixel groups in a binary image are used for processing, then processing speed is improved, but accuracy decreases due to inclusion of noise regions
Solution Approach 1:
The patent applies different evaluation criteria and validation thresholds to different candidate groups based on their local characteristics such as area ratio, density, and shape factors. This local quality approach allows the system to quickly identify and validate genuine character groups while rejecting noise regions, maintaining high processing speed through efficient local evaluation rather than uniform complex processing.
Solution Approach 2:
The patent enables candidate groups to self-validate through automated evaluation of their own characteristics against predefined criteria. Each candidate group is assessed based on its intrinsic properties (area, density, shape) without requiring manual intervention or complex external verification, allowing rapid automated filtering of noise regions while preserving genuine characters.
Data Source
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AI summary
There is provided an image processing apparatus in which a part used in predetermined processing is specified with use of a relatively small binarization threshold, from parts that have been converted into black pixels through binarization processing using a relatively large binarization threshold.