Deformed Document Sign Identification via Geometric Correction
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Solution Overview
Problem
Existing methods for identifying signs on deformed documents, such as lottery tickets, are sensitive to deformations like folding or crumpling and require mechanical smoothing or complex software projections, which are costly and prone to errors due to variations in brightness and contrast.
Innovation Solution
A method using an identification device with an acquisition module to capture digital images of deformed documents, a segmentation algorithm to determine candidate sign regions, and a descriptor calculation module to generate region signatures that are invariant to scale and rotation, allowing for accurate sign identification without mechanical smoothing or projection systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Shape
If mechanical smoothing is used to handle deformed documents, then document flatness is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces mechanical smoothing devices with a software-based document smoothing method. The system captures images of deformed documents and applies computational algorithms to correct geometric distortions, eliminating the need for mechanical rollers, pressure plates, or other physical smoothing mechanisms while achieving the same document flatness improvement.
Solution Approach 2:
The patent extracts and corrects only the necessary geometric deformation information from captured images using feature detection and transformation algorithms, rather than physically processing the entire document through complex mechanical systems. This selective extraction approach simplifies the overall system while maintaining document flatness.
2Reliability
If mask matching is used for sign identification, then sensitivity to global deformations is improved, but sensitivity to local deformations and lighting variations worsens
Solution Approach 1:
The patent segments the document into multiple regions and applies localized geometric correction transformations to each region based on detected feature points. This allows the system to handle both global document deformations and local variations in shape and size, improving sign identification accuracy while maintaining tolerance to various deformation types.
Solution Approach 2:
The patent applies different correction strategies to different regions of the document based on local deformation characteristics. By detecting feature points in each region and computing local transformation parameters, the system adapts to local variations in lighting, contrast, and geometric distortion, thereby improving measurement precision without sacrificing overall deformation tolerance.
3Device complexity
If software smoothing is used to correct document deformations, then device complexity is reduced, but computing power requirements increase
Solution Approach 1:
The patent applies software smoothing selectively to regions containing signs or features of interest, rather than processing the entire document image uniformly. By focusing computational resources on critical areas and using efficient algorithms for geometric correction, the system reduces overall computing power requirements while maintaining device simplicity.
4Measurement precision
If OCR or OMR techniques are used for sign detection, then position and content identification is improved, but sensitivity to document deformations worsens
Solution Approach 1:
The patent applies geometric correction transformations to the captured document image before performing OCR or OMR recognition. By pre-correcting deformations such as skew, scale variations, and local distortions based on detected feature points, the system ensures that subsequent sign detection and recognition operations work on a normalized image, thereby maintaining both position accuracy and deformation robustness.
Data Source
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AI summary
The present invention relates to a method for identifying a sign on an image of a document that may be distorted, comprising: - an acquisition (E1) of said digital image of said document;- a determination (E2) in the acquired image of at least one candidate sign region using an image segmentation algorithm, - for each candidate sign region, a calculation (E3) of a signature comprising information relating to the location in the acquired image of said candidate sign region and a region descriptor relating to local image features in said region, - an identification (E4) of a sign on the document image from the calculated signatures jointly comprising a comparison (E41) of the calculated signatures with reference signatures relating to sign regions of document models, said comparison being carried out as a function of a geometric deformation model of said document, and an estimation (E42) as a function of said comparison of said geometric deformation model.;