Barcode image recognition method and device using the same
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Solution Overview
Problem
Existing barcode recognition technologies often fail to accurately and efficiently decode barcode information from multiple barcodes, leading to decoding failures or errors when scanning interfaces with multiple barcodes.
Innovation Solution
A barcode image recognition method and device that captures and decodes barcode images, determines vertex coordinates, forms image boundaries, generates a detection line, and identifies the target image with an odd number of intersections to accurately output the barcode information of the expected barcode.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a barcode decoder receives multiple barcode images captured by a scanner, then the scanner can capture all barcodes in the field of view, but decoding failures or errors occur
Solution Approach 1:
The patent divides the multiple captured barcode images into separate processing units, applying image boundary detection and intersection counting algorithms to identify and process each barcode image independently. This segmentation allows the system to handle multiple barcodes systematically, selecting the target barcode image with odd number of intersections while ignoring others, thereby maintaining decoding accuracy even when multiple barcodes are captured simultaneously
Solution Approach 2:
The patent introduces an intermediary processing step between image capture and decoding: generating image boundaries from vertex coordinates and creating a detection beeline to count intersections. This intermediary mechanism acts as a filter to identify the target barcode image among multiple captured images, preventing decoding errors by ensuring only the intended barcode is processed
2Adaptability or versatility
If the system processes multiple barcode images to identify the target barcode, then the intended barcode can be selected, but the processing complexity increases
Solution Approach 1:
The patent transforms the barcode identification problem into a parameter-based selection process by extracting vertex coordinates from each barcode image, generating image boundaries, and counting intersection points with a detection beeline. The key parameter change is using the intersection count (odd vs. even) as the selection criterion, which provides a simple, objective rule for identifying the target barcode among multiple candidates without requiring complex image analysis or user intervention
Data Source
AI summary
A barcode image recognition method includes the following steps: capturing at least one barcode to generate a preview image, the preview image having at least one barcode image of the at least one barcode; decoding the barcode image to obtain barcode information and a plurality of vertex coordinates of the barcode image; caching the barcode information of the barcode image; forming a plurality of image boundaries of the barcode image with the plurality of vertex coordinates of the barcode image; generating a detection beeline from a specified point to a side edge of the preview image; determining the number of intersections of the plurality of image boundaries of the barcode image and the detection beeline; identifying the barcode image with odd number of intersections from the at least one barcode image as a target image; and outputting the barcode information of the target image.