Document Image Correction via Unified Neural Network
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Solution Overview
Problem
Current document image correction methods, such as global deformation parameter prediction and pixel-by-pixel deformation parameter prediction, face inefficiencies due to high computational costs and errors, especially when dealing with inconsistent pixel deformations, limiting their effectiveness and application scenarios.
Innovation Solution
A method and apparatus utilizing a correction model comprising a U-shaped convolutional neural network with multiple deformation parameter prediction sub-modules and a deformation correction module, trained on distorted image samples, to predict and correct pixel-level deformations in a single end-to-end process, reducing computational complexity and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel-by-pixel deformation parameter prediction is used, then correction accuracy for inconsistent pixel deformations is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent merges deformation parameter prediction with image restoration into a single unified network architecture. The network simultaneously predicts deformation parameters and restores the image in one forward pass, eliminating the need for separate prediction and restoration steps, thereby reducing computational complexity while maintaining pixel-level correction accuracy
Solution Approach 2:
The network is designed to perform multiple functions: it predicts deformation parameters for all pixels and simultaneously completes the image restoration task. This multi-functional approach allows the system to handle both the prediction of deformation fields and the actual image correction in a single model, reducing overall computational burden
2Reliability
If deformation parameter prediction and image restoration are performed as independent steps, then each step can be optimized separately, but the total number of calculations increases and processing time extends
Solution Approach 1:
The patent combines deformation parameter prediction and image restoration into a single integrated network that processes the input image in one forward pass to produce both deformation parameters and the restored image. This eliminates sequential processing steps and reduces total computational time while maintaining the reliability of separate optimization through unified training
3Productivity
If global deformation parameter prediction is used, then computational cost is reduced, but the ability to handle inconsistent pixel deformations is lost
Solution Approach 1:
The patent segments the deformation parameter prediction into pixel-level individual predictions while maintaining global context through the unified network architecture. Each pixel receives its own deformation parameter prediction based on local characteristics, enabling the system to handle inconsistent pixel deformations while keeping computational costs manageable through efficient network design
Data Source
AI summary
Embodiments of the present disclosure provide a method and apparatus for correcting a distorted document image, where the method for correcting a distorted document image includes: obtaining a distorted document image; and inputting the distorted document image into a correction model, and obtaining a corrected image corresponding to the distorted document image; where the correction model is a model obtained by training with a set of image samples as inputs and a corrected image corresponding to each image sample in the set of image samples as an output, and the image samples are distorted. By inputting the distorted document image to be corrected into the correction model, the corrected image corresponding to the distorted document image can be obtained through the correction model, which realizes document image correction end-to-end, improves accuracy of the document image correction, and extends application scenarios of the document image correction.