Barcode Reading Device Using Horizontal Area Segmentation
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Solution Overview
Problem
Existing code reading technologies face challenges in accurately reading barcodes from images photographed by cameras due to dirt or illumination light overlap, and similarly struggle with QR codes displayed on mobile screens affected by scratches or mirror reflections.
Innovation Solution
A code reading device divides the photographed barcode image into horizontal areas, extracts readable regions, and couples them to restore the whole barcode, enabling accurate reading even with dirt or illumination light interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a code reader captures an image of a barcode attached to a product package, then the barcode can be read in normal conditions, but the reading accuracy deteriorates when dirt sticks to the barcode or illumination light overlaps with the code in the photographed image
Solution Approach 1:
The photographed image of the barcode is divided into multiple horizontal areas, and each area is further divided into multiple blocks. This segmentation allows the system to process and evaluate different regions independently, identifying readable blocks even when some areas are contaminated by dirt or illumination light interference.
Solution Approach 2:
The system extracts readable blocks from the segmented areas by evaluating the readability of each block. Blocks that are not affected by dirt or illumination light are identified and extracted for decoding, separating the useful information from the contaminated regions.
2Measurement precision
If a code reader attempts to read a QR code displayed on a mobile terminal screen, then the code can be read when the screen is clean, but reading accuracy deteriorates when scratches or dirt stick to the screen or mirror reflection occurs due to incident angle of illumination light
Solution Approach 1:
The displayed QR code image is divided into multiple horizontal areas with each area containing multiple blocks. This segmentation enables the system to identify and select readable blocks from different regions, compensating for areas affected by screen scratches, dirt, or mirror reflection.
Solution Approach 2:
The system extracts readable blocks from the segmented areas by evaluating which blocks are successfully read. Blocks that are obscured by screen contaminants or reflection are excluded, and only the readable blocks are used for decoding the QR code.
3Device complexity
If the entire barcode image is processed as a single unit, then processing is simple, but reading fails when any part of the barcode is contaminated or obscured
Solution Approach 1:
The barcode image is divided into multiple horizontal areas, each containing multiple blocks. This segmentation increases processing complexity but significantly improves reliability by allowing the system to identify and read valid blocks even when other blocks are contaminated or obscured.
Solution Approach 2:
Different blocks within the segmented areas are evaluated individually for readability. Blocks with different quality characteristics (readable vs. contaminated) are treated differently, with only the readable blocks being used for decoding, thereby improving overall reading reliability.
Data Source
AI summary
A code reading device is equipped with a region extracting unit 2, which divides a photographed image of a bar code into a plurality of horizontal areas, and extracts readable regions from the horizontal areas respectively, and a region coupling unit 4, which couples the extracted regions to restore the whole bar code. Even if an unreadable portion occurs in a part of the bar code, a plurality of readable regions are extracted as parts of the barcode and those regions are coupled to restore a bar code the entirety of which can be read. Consequently, it is possible to accurately read a bar code having dirt or containment of illumination light partially.