Barcode Recognition via Image Enhancement and Context
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Solution Overview
Problem
Current barcode detection methods struggle with recognizing barcodes in low-quality images across large spatial areas, such as retail stores, due to factors like motion blur, poor illumination, and varying store layouts, which affects the efficiency of inventory management and signage placement.
Innovation Solution
A multifaceted detection process that includes image enhancement and auxiliary information processing to improve barcode recognition, using a mobile system with multiple cameras to capture and analyze images of product labels, and employing sub-image manipulation and contextual information to decode readable barcodes and identify unreadable ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If barcode detection is performed on low-quality images across large spatial areas, then coverage area increases, but recognition accuracy deteriorates due to motion blur, poor illumination, and varying store layouts
Solution Approach 1:
The patent segments the barcode detection process into multiple independent image quality improvement processes. Each process (e.g., deblurring, illumination correction, contrast enhancement) operates on the image separately, allowing the system to maintain high recognition accuracy across large spatial areas by processing different regions with appropriate enhancement techniques
Solution Approach 2:
The patent applies multiple image quality improvement processes that modify various parameters of the captured images. These include adjusting illumination parameters, motion blur correction parameters, and contrast parameters. By changing these parameters dynamically based on local image conditions, the system maintains accurate barcode recognition across diverse spatial areas with varying lighting and motion conditions
2Measurement precision
If multiple image quality improvement processes are applied to enhance barcode regions, then barcode recognition accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
The patent implements a selective image enhancement strategy where not all image quality improvement processes are applied to all images. Instead, the system evaluates each image or image region and applies only the necessary enhancement processes. This partial action approach maintains high barcode recognition accuracy while significantly reducing processing time by avoiding unnecessary computational steps
Solution Approach 2:
The patent segments the image processing workflow into distinct stages: initial image capture, quality assessment, selective enhancement application, and barcode decoding. By segmenting the process and applying enhancement only where needed based on quality metrics, the system achieves high recognition accuracy without the excessive processing time that would result from applying all possible enhancements uniformly
3Measurement precision
If manual sorting of signage is performed to match store product locations, then signage placement accuracy improves, but labor time and operational complexity increase
Solution Approach 1:
The patent replaces the manual mechanical sorting process with an automated optical system. Image capturing devices capture images of barcodes and product labels on store shelves, and processors automatically decode the barcodes and map product locations. This substitution of manual labor with automated image processing and decoding eliminates time-consuming manual sorting while maintaining high signage placement accuracy through precise optical detection and computational mapping
4Measurement precision
If high-quality imaging equipment is used to ensure clear barcode capture, then image quality improves, but system cost and device complexity increase
Solution Approach 1:
The patent achieves high image quality not through expensive specialized imaging equipment but by applying multiple image quality improvement processes that modify image parameters computationally. These processes correct for motion blur, poor illumination, and other degradation factors captured by simpler, less complex imaging devices. By changing image parameters through processing rather than requiring perfect capture conditions, the system maintains high image quality while using simpler, more cost-effective imaging hardware
Data Source
AI summary
This disclosure provides an image processing method and system for recognizing barcodes and/or product labels. According to an exemplary embodiment, the method uses a multifaceted detection process that includes both image enhancement of a candidate barcode region and other product label information associated with a candidate barcode region to identify a product label, where the candidate barcode region includes a nonreadable barcode. According to one exemplary application, a store profile is generated based on the identifications of the product labels which are associated with a location of a product within a store.