Image Segmentation for Barcode Decoding
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Solution Overview
Problem
Mobile devices struggle with efficiently decoding optical patterns, such as barcodes and QR codes, due to resource-intensive processing requirements and the need for quicker and more accurate scanning in various applications like shipping, retail, and warehousing.
Innovation Solution
Implementing an image analysis system using visual geometry and machine learning to segment images, detect specific patterns, and apply optical character recognition (OCR) only on relevant segments, thereby reducing computational resources and enhancing decoding speed and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optical character recognition is applied to the entire image, then character detection completeness is improved, but processing time and computational resources increase
Solution Approach 1:
The patent divides the image into multiple segments based on detected geometric features (such as rectangles, lines, or other structured patterns). OCR is then applied only to relevant segments rather than the entire image, reducing processing time while maintaining detection completeness. The segmentation is guided by visual geometry detection that identifies regions containing potential text or codes.
Solution Approach 2:
Different processing strategies are applied to different regions of the image based on local characteristics. Regions containing geometric features are processed with OCR, while other regions are either skipped or processed with different methods. This local differentiation optimizes resource allocation and processing speed.
2Measurement precision
If optical character recognition is applied to the entire image, then character detection completeness is improved, but computational resources increase
Solution Approach 1:
The image is segmented into multiple regions based on detected geometric features. OCR processing is restricted to only those segments that contain relevant geometric patterns, significantly reducing the computational load and energy consumption compared to processing the entire image, while still ensuring complete character detection in relevant areas.
Solution Approach 2:
Instead of applying OCR to the entire image (excessive action), the patent applies OCR only to specific segments containing geometric features (partial action). This partial processing approach reduces computational resources sufficiently while maintaining adequate detection completeness for the application's needs.
3Measurement precision
If traditional barcode scanning methods are used, then decoding accuracy is improved, but versatility and scalability are reduced
Solution Approach 1:
The patent implements a universal image analysis system that can handle multiple types of optical patterns (barcodes, QR codes, alphanumeric text, symbols) using a common architecture. The system detects geometric features and applies appropriate decoding methods based on the detected pattern type, providing both high accuracy and broad versatility across different applications such as shipping, retail, and warehousing.
Solution Approach 2:
The system dynamically adapts its processing approach based on the detected geometric features and pattern type. Rather than using a fixed scanning method, the system adjusts its analysis and decoding strategies in real-time according to the specific characteristics of each optical pattern encountered, maintaining high accuracy across diverse applications.
Data Source
AI summary
An image is acquired by a camera. The image has a first set of characters and a second set of characters. The first set of characters are classified as an identifier. The second set of characters are classified as data associated with the identifier. The image is divided to create an image segment. The image segment includes the first set of characters and not the second set of characters. The first set of characters are decoded in the image segment to generate a first character string. The second set of characters are decoded to generate a second character string. The first character string is linked to the second character string based on classifying the first set of characters as the identifier and the second set of characters as the data associated with the identifier.