Folded Document Image Correction for Accurate Content Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing document processing technologies struggle with misaligned, distorted, or complexly structured documents, such as those with pages in a fold, leading to inefficiencies in OCR and object detection due to perspective distortions and incomplete unfolding.
Innovation Solution
A machine learning model identifies multiple reference features within a document image, tolerates certain geometric permutations, and corrects distortions using projective transformations to enable efficient content extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCR and object detection are applied to distorted documents, then processing can be performed, but accuracy deteriorates due to perspective distortions and incomplete unfolding
Solution Approach 1:
The system performs preliminary distortion correction by detecting reference features (corners, edges) and applying geometric transformations to the document image before OCR and object detection. This preprocessing step removes perspective distortions and unfolds folded pages, creating a normalized image that significantly improves subsequent content extraction accuracy.
Solution Approach 2:
The patent introduces an intermediary processing stage between image capture and content extraction. This intermediate step includes distortion detection using reference features, geometric transformation calculation, and image warping to correct distortions. This mediator layer transforms the distorted input image into a corrected intermediate representation that enables accurate OCR and object detection.
2Measurement precision
If complex distortion correction is applied to all documents, then accuracy for distorted documents improves, but processing time increases
Solution Approach 1:
The system applies distortion correction selectively rather than universally. It first analyzes the document to detect reference features and determine whether significant distortion or folding is present. Correction is applied only when needed, using the minimum necessary transformation to achieve acceptable accuracy, thus avoiding unnecessary processing time for already well-aligned documents.
Solution Approach 2:
The patent dynamically adjusts correction parameters based on detected document characteristics. By analyzing reference feature positions and distortion patterns, the system adapts the geometric transformation parameters (rotation angle, scaling factors, warping coefficients) to match the specific document, achieving accurate correction with optimized processing effort for each case.
3Measurement precision
If multiple reference features are detected and processed, then distortion correction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the document analysis into distinct components: detection of individual reference features (corners, edges, lines), classification of feature types, and separate processing for each feature category. This segmentation allows the system to handle complex documents by breaking them into manageable feature units, improving detection accuracy while organizing computational complexity into modular, efficient processing stages.
Data Source
AI summary
Aspects and implementations provide for techniques of fast and efficient detection of depictions in multi-page documents and documents having complex structure. The disclosed techniques include processing an image of a document to generate probability distributions (PDs) predicting reference features (RFs) of the document. The model is trained using a first PD-to-RF mapping that samples RFs using training PDs generated for a training image. The techniques further include predicting the RFs using a second PD-to-RF mapping that determines the RFs based characteristics of the individual PDs. The techniques further include generating, using the predicted of RFs, a corrected image of the document, and extracting, using the corrected image, a content of the document.