Undistorting Deformed Document Images via Isolines and Reverse Transformation
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Solution Overview
Problem
Images of deformed indicia-bearing surfaces, such as folded or crumpled paper, are often distorted, making it difficult for image processing tasks like OCR and structure analysis.
Innovation Solution
A method and system that process images by detecting image fragments, grouping them by lines of text, generating a map of isolines to identify distortion, and applying a reverse transformation matrix to remove distortions and reconstruct an undistorted image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an image of a deformed indicia-bearing surface is acquired directly, then the image acquisition process is simple and fast, but the resulting image is distorted and unusable for accurate processing
Solution Approach 1:
The system performs preliminary detection of text fragments and generation of isolines before the actual undistortion process. By pre-identifying the locations and orientations of text fragments and generating isolines that represent the deformation field, the system prepares the necessary transformation data in advance, enabling accurate undistortion while maintaining a systematic processing flow.
Solution Approach 2:
The isolines act as an intermediary representation between the distorted image and the undistorted output. These isolines encode the deformation characteristics and serve as a intermediate data structure that guides the reverse transformation process, bridging the gap between the deformed input and the corrected output image.
2Measurement precision
If traditional image processing methods are applied to distorted images, then the processing steps are simple, but the results are inaccurate for OCR and structure analysis
Solution Approach 1:
The system segments the distorted image into multiple text fragments and processes each fragment independently to determine its orientation and position. This segmentation allows the system to handle complex deformations by breaking them down into manageable local transformations, which are then integrated through isolines to produce the overall undistorted image, improving OCR accuracy while maintaining reasonable processing time.
3Adaptability or versatility
If the indicia-bearing surface is deformed (folded, crumpled, wrinkled), then the document can be handled flexibly, but the resulting image distortion makes it difficult to perform image processing tasks
Solution Approach 1:
The system changes the parameter representation of the image by transforming from a simple pixel grid to a coordinate system defined by isolines. This parameter transformation allows the system to adapt to various deformation types (folding, crumpling, wrinkling) by modeling the deformation field through isolines, thereby maintaining ease of operation for image processing tasks even when the physical document is handled flexibly.
Data Source
AI summary
An example method of processing images of deformed indicia-bearing surfaces includes: detecting, within a document image, a plurality of image fragments, wherein each image fragment of the plurality of image fragment contains a respective sequence of alphabet symbols; grouping the plurality of image fragments by lines of text to be reconstructed in the document image; generating a map of isolines associated with the document image, wherein an isoline identifies a set of points that lie on a straight line of an undistorted image corresponding to the document image; generating a reverse transformation matrix that defines a set of transformations to be applied to the document image in order to remove image distortions caused by deformations of an indicia bearing surface; and generating an undistorted document image by applying the reverse transformation matrix to the document image.