Barcode Image Digitization Using Dynamic Threshold
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Solution Overview
Problem
Conventional barcode image digitization methods face challenges with noise, image shaky effects, and blur, leading to increased processing time and decoding errors due to complex enhancement requirements and potential error propagation.
Innovation Solution
A method and system that capture barcode images using an image sensor, generate sample lines, create reference lines, interpolate curves, and generate effective sample shapes to digitize the image into binary data using a dynamic threshold, reducing noise and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional barcode image enhancement methods are used to improve digitization accuracy, then measurement precision is improved, but device complexity and processing time increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the threshold value based on local image characteristics (standard deviation and mean intensity) rather than using a fixed global threshold. This allows the digitization process to adapt to varying image conditions, improving accuracy without requiring complex multi-step enhancement procedures. The dynamic threshold formula T(x,y) = α·σ(x,y) + β·μ(x,y) - γ changes parameters locally across the image to optimize digitization.
Solution Approach 2:
The enhancement process serves itself by using the raw image data directly to compute the dynamic threshold without requiring pre-processing steps. The algorithm automatically adapts to noise and blur conditions by calculating local statistical properties, eliminating the need for separate noise filtering or sharpening operations that would increase device complexity.
2Measurement precision
If conventional barcode image enhancement methods are used to improve digitization accuracy, then measurement precision is improved, but processing time increases
Solution Approach 1:
The patent segments the image processing into independent pixel-wise operations where each pixel's threshold is calculated based on its local neighborhood statistics. This segmentation allows for parallel computation and avoids the need for iterative global enhancement processes, reducing processing time while maintaining accuracy through localized adaptation.
Solution Approach 2:
The patent performs preliminary computation of local statistical properties (standard deviation and mean intensity) for each pixel neighborhood before applying the thresholding decision. This preliminary action organizes the computation in an efficient sequence that minimizes redundant calculations and enables fast processing while ensuring accurate digitization.
3Device complexity
If fixed threshold digitization is used, then device complexity is reduced, but measurement precision and adaptability to noise deteriorate
Solution Approach 1:
The patent introduces dynamics by making the threshold value adaptive rather than fixed. The threshold dynamically adjusts based on local image characteristics (noise level indicated by standard deviation and intensity indicated by mean), allowing the system to automatically adapt to varying conditions without increasing overall device complexity. The dynamic threshold formula T(x,y) = α·σ(x,y) + β·μ(x,y) - γ enables flexibility while maintaining computational efficiency.
Solution Approach 2:
The patent changes the threshold parameter from a constant value to a variable that depends on local image statistics. By computing the standard deviation and mean intensity for each pixel's neighborhood and using these to determine the threshold, the system achieves high measurement precision while keeping the algorithm relatively simple and computationally efficient.
4Reliability
If error propagation prevention is implemented through complex verification, then reliability is improved, but processing time and device complexity increase
Solution Approach 1:
The patent applies beforehand cushioning by using the dynamic threshold to prevent digitization errors before they can propagate to the decoding stage. By adapting the threshold to local image conditions, the method proactively compensates for noise and blur effects, reducing the likelihood of errors that would require subsequent verification and correction steps.
Data Source
AI summary
After capturing a barcode image, generate at least one sample line accordingly, acquire sample data of the at least one sample line, generate a first and a second reference lines, generate an upper bound and a lower bound according to the first and the second reference lines, generate a first curve by interpolating all sample points, generate a plurality of effective sample shapes of the first curve according to the upper bound and the lower bound, identify an internal point of each effective sample shape, generate a second curve by interpolating all internal points, and digitize the barcode image to binary data by comparing sample data with the second curve.