Image Processing Device Binarizing Threshold Determination
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Solution Overview
Problem
Conventional image processing technologies fail to adequately set threshold values for generating suitable binary image data from image data containing text, leading to suboptimal rendering of text in compressed images and character recognition processes.
Innovation Solution
An image processing device and method that employs a scanner driver to identify text regions, set appropriate threshold values based on edge detection, color analysis, and text sharpness, and generate binary image data using a combination of edge image processing and histogram analysis to distinguish text from background, ensuring accurate text representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional thresholding methods are used to generate binary image data, then the processing speed is fast, but the text rendering quality is insufficient
Solution Approach 1:
The patent segments the image processing into multiple stages: edge detection to identify text regions, histogram analysis to determine threshold values, and binary conversion to generate final binary image data. This segmentation allows each stage to be optimized independently, improving text rendering quality while managing processing complexity.
Solution Approach 2:
The patent performs preliminary edge detection and histogram analysis before the actual binary conversion. By pre-identifying text regions and determining appropriate threshold values in advance, the system ensures high text rendering quality in the final binary image without compromising overall processing efficiency.
2Measurement precision
If simple thresholding is applied, then the processing is fast, but the accuracy of character recognition is reduced
Solution Approach 1:
The patent replaces simple mechanical thresholding with a more sophisticated system combining edge detection algorithms and histogram analysis. This substitution improves character recognition accuracy by better distinguishing text from background, while the automated nature of the algorithms minimizes additional processing time.
Solution Approach 2:
The system performs self-adjustment by automatically determining optimal threshold values through histogram analysis of the input image. This eliminates the need for manual threshold selection and reduces processing time compared to iterative methods, while significantly improving character recognition accuracy.
3Manufacturing precision
If adaptive thresholding methods are used, then the text rendering is improved, but the computational complexity increases
Solution Approach 1:
The patent applies local adaptive thresholding by analyzing histograms within specific regions of the image rather than using a global threshold. This allows the binary image quality to be optimized locally for different text regions, while the regional approach keeps computational complexity manageable compared to pixel-by-pixel adaptive methods.
Solution Approach 2:
The patent changes the threshold parameter dynamically based on histogram analysis of the input image characteristics. By adjusting the threshold value according to the specific image content rather than using a fixed value, the system improves binary image quality while maintaining algorithmic simplicity through direct histogram-based calculation.
Data Source
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AI summary
In an image processing device, the binary image data generating portion is configured to generate binary image data representing the letter in the target image by using target image data. A background color identifying portion is configured to identify a background color value representing color of background of the target image. The letter color identifying portion is configured to identify a letter color value representing color of the letter. The characteristic value determining portion is configured to determine a characteristic value concerning a sharpness of the letter in the target image. The threshold value determining portion is configured to determine a binarizing threshold value by using the background color value, the letter color value, and the characteristic value. The binarizing process performing portion is configured to perform a binarizing process on the target image data by using the binarizing threshold value to generate the binary image data.