Text Image Shadow Segmentation for Clear Character Recovery
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Solution Overview
Problem
Text images captured with shooting equipment often suffer from inconsistent brightness and shadow occlusion, particularly in low-light conditions, leading to loss of information and poor reading quality.
Innovation Solution
A method involving grayscale conversion, histogram statistics, convex height sequencing, and mask representation processing to separate shadow and non-shadow areas, followed by background enhancement and fusion to eliminate shadows in text images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If text images are captured in low-light conditions with a single light source, then the shooting process can be simplified, but shadow occlusion and inconsistent brightness occur leading to information loss
Solution Approach 1:
The patent segments the text image into shadow areas and non-shadow areas using threshold segmentation. By dividing the image into distinct regions, the method can apply different processing techniques to each area, effectively recovering text information in shadow regions while preserving quality in non-shadow regions.
Solution Approach 2:
The patent performs preliminary shadow detection and area segmentation before final image processing. By identifying shadow regions in advance through histogram analysis and thresholding, the method can prepare appropriate restoration strategies for each area, preventing information loss before it occurs.
2Productivity
If direct printing of shadowed text images is performed, then the process is simple and fast, but the reading quality and information completeness deteriorate
Solution Approach 1:
The patent applies partial processing by focusing enhancement operations only on shadow areas rather than the entire image. Through selective processing of identified shadow regions using mask representations, the method achieves effective shadow elimination without the computational overhead of processing the whole image, thus maintaining productivity while improving reading quality.
3Reliability
If shadow elimination processing is applied to text images, then reading quality and information completeness improve, but the processing complexity increases
Solution Approach 1:
The patent employs automatic shadow detection and segmentation that uses the image's own statistical properties (histogram distribution, convex height sequencing) to identify shadow regions without requiring external reference images or manual intervention. This self-service approach reduces processing complexity by eliminating the need for complex external data or manual annotation.
Solution Approach 2:
The patent transforms the shadow elimination problem into a parameter-based solution by using histogram statistics and convex height sequencing to automatically determine segmentation thresholds. By changing from a complex pattern recognition approach to a statistical parameter-based approach, the method simplifies processing while maintaining high information recovery quality.
Data Source
AI summary
Disclosed are a shadow elimination method and apparatus for a text image, and an electronic device. The method comprises the following steps: converting a text image to be processed into a first grayscale image; performing convex height sorting processing on the first grayscale image to determine a background grayscale segmentation threshold of the first grayscale image and a text grayscale segmentation threshold within a shadow region of the first grayscale image; processing the background grayscale segmentation threshold and the text grayscale segmentation threshold to respectively obtain a shadow region mask representation and a non-shadow region mask representation; performing background enhancement on the first grayscale image to obtain a second grayscale image; and performing fusion processing on the shadow region having the background enhancement in the second grayscale image and the non-shadow region in the first grayscale image according to the shadow region mask representation and the non-shadow region mask representation.