Character Segmentation with Edge and Contour Detection for Noisy Images
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Solution Overview
Problem
Existing character segmentation methods struggle with poor quality images from older digital meters due to factors like rust, stains, and reflections, leading to false edges or character adhesion, which affect accuracy in remote meter reading systems.
Innovation Solution
A character segmentation method combining edge detection and contour detection algorithms, including filtering, adaptive parameter adjustment, and projection techniques to enhance accuracy on poor quality images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If edge detection approach is employed with loose parameter setting, then more complete character edges can be obtained, but many false edges caused by stains or reflections will be introduced
Solution Approach 1:
The patent segments the image processing into distinct stages: grayscale conversion, binary conversion, edge detection, and false edge elimination. By dividing the processing pipeline, each stage can be optimized independently to balance edge completeness and false edge reduction.
Solution Approach 2:
The patent performs preliminary actions before edge detection by converting the image to grayscale and then to binary image. This preliminary processing reduces noise and standardizes the image data, enabling more accurate edge detection with fewer false edges.
2Object-affected harmful factors
If edge detection approach is employed with strict parameter setting, then false edges are reduced, but loss of edge information of target characters occurs
Solution Approach 1:
The patent implements a feedback mechanism where the binary conversion parameters are adjusted based on the characteristics of the original image. The system analyzes the image quality and adaptively sets the binary conversion threshold to preserve edge information while reducing false edges.
3Manufacturing precision
If histogram thresholding approach or clustering technique is employed, then binarization is achieved, but character adhesion problem occurs
Solution Approach 1:
The patent changes the parameter space by transitioning from direct thresholding methods to an edge-based approach. By detecting edges first and then eliminating false edges, the method achieves both accurate binarization and reliable character segmentation without adhesion.
Data Source
AI summary
A character segmentation method and apparatus, and a computer-readable storage medium are provided. The method includes converting a character area image into a grayscale image; converting the grayscale image into an edge binary image by edge detection; acquiring character box segmentation blocks from the edge binary image by projection; and determining a target character area from the character box segmentation blocks by contour detection, and performing character segmentation on the character area image according to the target character area; or comprises: converting a character area image into a grayscale image; performing clustering analysis on the grayscale image by fuzzy C-means clustering, and binarizing the grayscale image according to the analysis result; acquiring character positioning blocks from a binary image by projection; and performing character segmentation on the character area image according to position information of the character positioning blocks. Character segmentation can be performed on a relatively low quality image.