Imaging Barcode Reader Pattern Generation
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Solution Overview
Problem
Handheld bar code scanners face challenges with blurry images, distortion, and uneven illumination, making the efficient decoding of text-based bar codes difficult, especially when dealing with unknown formats, which requires user training and is prone to errors.
Innovation Solution
An imaging-based bar code reader system that automates the generation of a pattern for unknown optical character recognition (OCR) strings by scanning template samples, allowing for efficient and user-friendly operation without the need for user training, by analyzing and recording the format of decoded data for accurate reading of similar strings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If handheld bar code scanners are used, then portability and ease of operation are improved, but image quality deteriorates resulting in blurry images, distortion, and uneven illumination
Solution Approach 1:
The system performs preliminary actions by capturing multiple images and automatically selecting or combining the best quality images before decoding. This preliminary image selection and processing step ensures that even with handheld portability, the final decoded output maintains high accuracy by pre-processing images to eliminate blur and distortion issues.
Solution Approach 2:
The system creates multiple copies of the target bar code through repeated image capture attempts. By capturing multiple images and selecting the highest quality copy, the system overcomes the image quality deterioration inherent in handheld scanning while maintaining portability benefits.
2Ease of operation
If automated pattern generation is implemented, then user training requirements are reduced and errors decrease, but system complexity increases
Solution Approach 1:
The system performs self-service by automatically generating decoding patterns from captured images without requiring user input or training. The automated pattern generation algorithm analyzes the bar code structure independently, eliminating the need for users to learn complex formatting rules while the integrated automation manages the increased system complexity internally.
Solution Approach 2:
The system changes parameters dynamically by adjusting decoding thresholds, image processing parameters, and pattern recognition settings based on the specific characteristics of each captured image. This adaptive parameter adjustment enables automated operation across diverse bar code formats without requiring user training, while the system internally manages the complexity of parameter optimization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system significantly reduces errors and enhances efficiency in reading text-based bar codes by automating the pattern generation and format recognition, enabling accurate decoding of unknown strings without requiring user input or extensive training.
Implementation Method 1
Light reflected from the target bar code is focused through a lens of the imaging system onto the pixel array
Implementation Method 2
Light reflected from the target bar code is focused through a lens of the imaging system onto the pixel array
Implementation Method 3
An imaging system include charge coupled device (CCD) arrays, complementary metal oxide semiconductor (CMOS) arrays, or other imaging pixel arrays having a plurality of photosensitive elements or pixels
Data Source
AI summary
An imaging-based bar code reader that includes an imaging and decoding system. Focusing optics and a sensor array define a field of view. A data processor has a memory for storing a pattern definition of previously imaged OCR characters and comparing a format of said previously stored characters to a present image to determine a character content of the present image.