Automatic Commodity Price Tag Association via Image Recognition
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Solution Overview
Problem
In shopping malls, the information on price tags often differs from the data stored in barcodes, causing management inefficiencies and inaccuracies in commodity information display.
Innovation Solution
A method and system for automatic association of commodities and price tags, involving picture acquisition, unit detection, barcode identification, and data association, which includes identifying commodity and price tag zones, reading barcode information, and storing corresponding data in a database, with optional alarm signals for inconsistencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If bar code information is printed on price tags manually, then price tags can be displayed on commodities, but the information on price tags becomes inconsistent with the barcode data
Solution Approach 1:
The patent replaces manual mechanical operations (printing barcodes and text on price tags) with automated image recognition and data processing systems. The system captures images of price tags and commodities, automatically extracts barcode and text information through recognition algorithms, and verifies consistency between them, eliminating human error and improving information reliability.
Solution Approach 2:
The system enables self-service by allowing the price tag information to verify itself against the commodity barcode automatically. The image recognition system extracts data from both the price tag and commodity barcode, then automatically compares them to detect inconsistencies without requiring manual intervention, making the verification process self-contained and reliable.
2Measurement precision
If manual verification of price tag information is performed, then information accuracy can be checked, but management efficiency decreases
Solution Approach 1:
The patent replaces manual verification processes with automated image recognition and data comparison systems. The system automatically captures images, extracts barcode and text information, and verifies consistency between price tags and commodity data through computational algorithms, maintaining high accuracy while dramatically improving management efficiency by eliminating time-consuming manual checks.
Solution Approach 2:
The system creates digital copies of price tag and commodity information through image capture and recognition. These digital copies are then automatically compared and verified against the original data sources, enabling rapid and accurate information validation without requiring physical manual inspection, thus preserving accuracy while enhancing productivity.
3Measurement precision
If automated image recognition is used to identify commodities and price tags, then association accuracy improves, but system complexity increases
Solution Approach 1:
The patent employs a multi-functional integrated system where a single image recognition platform performs multiple tasks: capturing images of commodities and price tags, extracting barcode information, recognizing text content, verifying data consistency, and generating verification results. This universal system approach improves association accuracy while managing complexity by consolidating multiple functions into one cohesive system rather than separate independent components.
Data Source
AI summary
A method for automatic association of commodities and price tags, comprises: S1: acquiring several pictures, carrying out unit detection on each one of the pictures, and identifying a plurality of commodity zones within each one of the pictures; S2: carrying out price tag detection on each one of the pictures, and identifying a plurality of price tag zones within each one of the pictures; S3: identifying a bar code in each one of the price tag zones, reading first commodity information corresponding to each one of the bar codes, and verifying the commodity zone corresponding to the bar code in each one of the price tag zones; S4: associating the commodity zone of each one of the bar codes with the corresponding first commodity information, and then storing the associated data into a database.