Neural Network Texture Extraction for Curved Surface OCR
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Solution Overview
Problem
Computers face difficulties in reading information from curved or off-angle surfaces due to distortions introduced by 3-D perspectives, which hampers Optical Character Recognition (OCR) and watermark detection algorithms.
Innovation Solution
A method that uses a neural network to convert primary images from curved or off-angle surfaces into secondary images with minimized distortions, employing UV mapping grids and alpha images to rectify the texture, allowing for easier processing by OCR and code reading algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computers use standard image processing to read information from curved or off-angle surfaces, then the process is simple and fast, but the reading accuracy deteriorates due to 3-D perspective distortions
Solution Approach 1:
The system performs preliminary actions by detecting the 3-D geometry and perspective distortions of the target surface before attempting to read information. It calculates transformation parameters and applies geometric corrections to the image data in advance, converting distorted perspectives into corrected representations that preserve information integrity for subsequent accurate reading.
2Measurement precision
If computers apply geometric correction to eliminate 3-D perspective distortions, then reading accuracy improves, but processing time increases
Solution Approach 1:
The system replaces complex mechanical geometric correction processes with computational methods. It uses digital image processing algorithms to calculate and apply perspective transformations, substituting iterative geometric adjustments with direct mathematical computations that achieve the same correction效果 more efficiently.
Solution Approach 2:
The system changes parameters by detecting and utilizing the specific 3-D geometry parameters of the target surface. It extracts transformation parameters from the detected geometry and applies these parameters to correct the image, adapting the processing to the specific geometric characteristics rather than using fixed correction methods.
3Productivity
If computers process images from curved surfaces without correction, then processing is fast, but information extraction accuracy deteriorates
Solution Approach 1:
The system performs preliminary detection of 3-D geometry and calculation of transformation parameters before information extraction. This preliminary processing establishes the correct geometric framework in advance, enabling fast and accurate information extraction without requiring iterative corrections during the reading phase.
Data Source
AI summary
Texture extraction is disclosed.