License Plate Character Extraction Using Local Contrast Binarization
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Solution Overview
Problem
Existing license plate recognition systems face challenges in maintaining a high recognition rate due to variations in brightness distribution caused by changes in position or lighting conditions, leading to inconsistent character separation and recognition.
Innovation Solution
A method involving converting pixel values in a grayscale image by comparing them to the average of surrounding pixels, generating a refined image through binarization, and extracting characters using template matching, ensuring consistent recognition regardless of lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If edge detection and binarization methods are used to separate license plate characters, then the separation process is simple and fast, but the recognition rate is not guaranteed when brightness distribution changes due to lighting or position variations
Solution Approach 1:
The patent changes the parameter used for character separation from fixed global threshold values to dynamic local contrast values. By comparing each pixel's intensity with its surrounding pixels and calculating local contrast, the method adapts to varying brightness distributions caused by different lighting conditions and camera positions, thereby maintaining high recognition rates while preserving computational efficiency.
Solution Approach 2:
The patent applies local quality by performing character separation based on local pixel contrast rather than global thresholding. Each pixel's value is compared with its neighboring pixels to determine local contrast, allowing the separation process to adapt to local brightness variations across different regions of the license plate image, thus improving recognition reliability under varying lighting conditions.
2Device complexity
If a fixed threshold value is used for binarization, then the processing is computationally simple, but the character separation fails when lighting conditions vary
Solution Approach 1:
The patent introduces dynamics by replacing static fixed threshold values with dynamic local contrast calculations. The threshold for each pixel is determined by its local neighborhood statistics rather than a predetermined global value, enabling the binarization process to automatically adapt to varying lighting conditions while maintaining reasonable computational complexity through efficient local comparisons.
Solution Approach 2:
The patent implements self-service by enabling the image processing algorithm to automatically adapt to different lighting conditions without requiring manual threshold adjustment. The local contrast calculation uses each pixel's own neighborhood to determine its threshold, allowing the system to self-adjust to varying illumination, camera positions, and environmental conditions.
Data Source
AI summary
There is provided a method of extracting characters from a license plate of a vehicle performed by a license plate character extraction device. The method comprises: converting a input image obtained by capturing the license plate of the vehicle into a grayscale image; generating a converted image based on a result of comparing a value of at least one pixel included in the grayscale image with a first average of values of pixels adjacent to the at least one pixel; generating a refined image based on a result of comparing the converted image with a binarized image obtained by binarizing the converted image; and extracting characters included in the refined image.