Barcode Decoding With Hypothesis Grids for Distorted Images
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Solution Overview
Problem
Existing barcode decoding technologies struggle with images of reduced quality, perspective distortions, missing or obscured symbols, and sub-optimal lighting conditions, leading to inefficient and unreliable decoding of one-dimensional and two-dimensional barcodes.
Innovation Solution
The implementation involves constructing hypotheses based on pixel intensity histograms to identify barcode lines, using neural networks to process and rescale images, and adjusting module grids to enhance decoding accuracy, particularly for one-dimensional, two-dimensional, and stacked linear barcodes under adverse conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional barcode decoding methods are used, then decoding speed is maintained, but decoding reliability deteriorates under unfavorable conditions such as reduced image quality, perspective distortions, and sub-optimal lighting
Solution Approach 1:
The patent segments the barcode decoding process into multiple independent modules: image quality assessment module, hypothesis generation module, similarity factor calculation module, and hypothesis selection module. Each module processes specific aspects of the decoding task, allowing the system to handle unfavorable conditions systematically while maintaining modular complexity management.
Solution Approach 2:
The patent performs preliminary actions by generating multiple hypotheses about barcode line groupings before final decoding. The system pre-calculates similarity factors for each hypothesis against reference symbols, and pre-assesses image quality metrics before committing to a decoding path. This preliminary processing enables more reliable decoding under adverse conditions by exploring multiple possible interpretations.
2Measurement precision
If multiple hypotheses are generated and evaluated, then decoding accuracy improves under adverse conditions, but computational time increases
Solution Approach 1:
The patent applies partial action by evaluating multiple hypotheses but selecting only the top-ranked hypothesis (or a limited number) based on aggregated similarity factors. Rather than exhaustively processing all possible hypotheses, the system performs sufficient evaluation to identify the most likely correct grouping, balancing accuracy improvement with computational efficiency.
Solution Approach 2:
The patent implements feedback mechanisms where the similarity factor calculations provide quantitative feedback on how well each hypothesis matches reference barcode symbols. This feedback guides the hypothesis selection process, allowing the system to efficiently identify accurate decodings by following the feedback signal from similarity measurements to hypothesis ranking.
3Reliability
If histogram-based line identification is used, then robustness to lighting variations improves, but sensitivity to noise increases
Solution Approach 1:
The patent introduces histogram-based intensity distribution analysis as an intermediary between raw pixel data and line identification. Instead of directly processing pixel values, the system uses histograms to represent the distribution of intensities, which serves as a mediator that preserves lighting variation robustness while filtering out some noise through statistical aggregation across multiple pixels.
Solution Approach 2:
The patent changes the parameter representation from individual pixel intensities to histogram-based intensity distributions. This parameter transformation allows the system to maintain robustness to lighting variations (since histograms capture overall distribution rather than absolute values) while reducing noise sensitivity through the averaging effect inherent in histogram construction.
Data Source
AI summary
Aspects and implementations provide for mechanisms of detection and decoding of barcodes in images. The disclosed techniques include estimating dimensions of a module of a barcode based on geometric characteristics of a barcode image, forming hypotheses that group modules into barcode symbols, and assessing viability of formed hypotheses. Various operations of the techniques may involve the use of neural networks, including estimation of module dimensions and assessment of groupings of modules into lines and lines into barcode symbols. The techniques may be used for decoding of barcodes captured in images of unfavorable conditions, including blur, perspective, sub-optimal lighting, barcode deformation, and the like. The techniques may be applied to decoding linear one-dimensional barcodes, two-dimensional barcodes, and stacked linear barcodes.