Graph Lattice for Efficient Document Recognition
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Solution Overview
Problem
Existing document recognition systems face challenges in efficiently handling image noise and variability due to their reliance on subgraph matching techniques, which become exponentially expensive and inefficient when dealing with large subgraphs, limiting their effectiveness in image categorization, retrieval, and repeated structure detection.
Innovation Solution
A graph lattice system is introduced, where images are represented as a collection of primitive features with their spatial relations, allowing for efficient exact graph matching through a lattice structure that captures image structure, facilitating clustering, classification, retrieval, and novelty detection by only building meaningful subgraphs from a data corpus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subgraph matching techniques are used for image categorization and retrieval, then image structure and spatial relationships can be captured, but the computational complexity becomes exponentially expensive especially for large subgraphs
Solution Approach 1:
The patent segments the graph matching problem into multiple levels of a lattice structure, where nodes represent subgraphs of increasing complexity. Instead of performing exhaustive matching on large subgraphs directly, the system breaks down the matching process into smaller incremental steps through the lattice hierarchy, computing matches level by level from simple to complex subgraphs.
Solution Approach 2:
The patent performs preliminary actions by pre-computing and storing subgraph matches in the lattice structure before actual image querying. The lattice is built in advance from a corpus of images, organizing all possible subgraph relationships ahead of time. This allows the system to avoid performing expensive matching computations during actual image retrieval operations.
2Reliability
If inexact graph matching techniques are used to handle noise and variability, then robustness to image variations is improved, but matching cost drastically increases and advantages of attributed graph matching are removed
Solution Approach 1:
The patent applies local quality by using attributed graphs where nodes and edges have specific properties that constrain possible matches. Instead of allowing any node to match any other node (which would require exhaustive exploration), the attributes provide local constraints that guide the matching process. This allows the system to handle noise and variability while maintaining efficiency, as mismatches can be quickly ruled out based on attribute incompatibility.
Solution Approach 2:
The patent uses partial action by computing only the necessary subgraph matches needed for accurate classification, rather than computing all possible matches. The lattice structure allows the system to stop computing at the level of detail required for accurate image categorization, avoiding the excessive computation that would result from computing all possible subgraph matches including those beyond what is needed for the task.
3Loss of information
If all possible subgraphs are considered for matching, then comprehensive image analysis is achieved, but the data structure becomes unwieldy and computationally infeasible
Solution Approach 1:
The patent implements the nested doll principle through the lattice structure, where smaller subgraphs are nested within larger subgraphs in a hierarchical organization. Each node in the lattice represents a subgraph that contains and is contained by other subgraphs at adjacent levels. This nested organization allows comprehensive image feature analysis while keeping the data structure manageable, as the hierarchy naturally groups related features and avoids redundant representation.
Solution Approach 2:
The lattice structure serves multiple functions simultaneously: it organizes subgraphs hierarchically, stores match results efficiently, enables incremental computation, and supports various image analysis tasks (categorization, retrieval, repeated structure detection). This multi-functionality reduces overall system complexity compared to separate structures for each function.
Data Source
AI summary
A document recognition system and method, where images are represented as a collection of primitive features whose spatial relations are represented as a graph. Useful subsets of all the possible subgraphs representing different portions of images are represented over a corpus of many images. The data structure is a lattice of subgraphs, and algorithms are provided means to build and use the graph lattice efficiently and effectively.