Document Clustering via Grid Feature Extraction and Numeric Thumbnails
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Solution Overview
Problem
Document receiving organizations face challenges in sorting and clustering vast quantities of scanned documents with image artifacts and unknown types, as existing methods require predefined cluster numbers and are unreliable due to random and varied document conditions.
Innovation Solution
A system and method that subdivides scanned images into a grid, analyzes grid elements for features, generates numeric thumbnails, and calculates similarity scores to automatically cluster documents without predefining the number of clusters, allowing for efficient grouping and organization of similar documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If known clustering routines are used, then documents can be grouped into clusters, but the number of clusters must be predefined which is not applicable when document types are unknown
Solution Approach 1:
The patent segments the clustering process into multiple stages: first extracting document features (text, layout, visual elements), then generating candidate clusterings with different numbers of clusters, and finally evaluating and selecting the optimal clustering. This segmentation allows the system to handle unknown document types without requiring a predefined cluster number, as it evaluates multiple possibilities systematically.
Solution Approach 2:
The patent implements a dynamic clustering approach where the number of clusters is not fixed but determined through evaluation of multiple candidate clusterings. The system dynamically adjusts the clustering structure by generating candidates with varying cluster counts and selecting the best one based on evaluation metrics, making the system adaptable to unknown document types while managing complexity through structured evaluation.
2Measurement precision
If pixel and location checking techniques are used, then document comparison can be performed, but image artifacts and distortions make these techniques difficult to apply
Solution Approach 1:
The patent extracts meaningful features from documents by separating them from artifacts and distortions. It extracts text content, layout structure, and visual elements as distinct feature types, then compares these extracted features rather than raw pixel data. This extraction approach eliminates the harmful effects of image artifacts and distortions while maintaining comparison precision.
Solution Approach 2:
The patent applies different comparison strategies to different feature types: text content is compared using string matching and semantic analysis, layout structure is compared using spatial relationship analysis, and visual elements are compared using image processing techniques. This local quality approach tailors the comparison method to each feature type, maintaining precision while being robust to artifacts and distortions that affect different features differently.
3Reliability
If manual processing and user intervention are used, then clustering accuracy can be improved, but processing time and productivity are reduced
Solution Approach 1:
The patent implements a self-service evaluation system that automatically generates multiple candidate clusterings, evaluates them using objective metrics, and selects the optimal clustering without requiring user intervention. The system serves itself by having built-in evaluation mechanisms that assess clustering quality based on feature similarity and cluster coherence, eliminating the need for manual review while maintaining reliability through systematic automated evaluation.
Solution Approach 2:
The patent incorporates feedback mechanisms where the evaluation results of candidate clusterings are used to refine and select the final clustering. The system uses evaluation metrics that provide feedback on clustering quality, allowing automated selection of the most reliable clustering configuration. This feedback loop enables the system to achieve high reliability through automated decision-making, maintaining productivity by eliminating manual intervention cycles.
Data Source
AI summary
A system for organizing a plurality of candidates based on the relative similarity of a first candidate with respect to the remaining plurality of candidates is disclosed. The system includes a controller in communication with a storage device configured to receive and accessibly store a generated plurality of candidate images. The controller operable to analyze each of the plurality of candidate images to determined a numeric thumbnail based on a number of identified features in each of a plurality of grid elements of an array and a sum total number of all identified features in the array, calculate a similarity score between one of the plurality of determined numeric thumbnails and each of the remaining plurality of determined numeric thumbnails; and generate a logical group image order as a function of the highest similarity score between the one of the plurality of determined numeric thumbnails and each of the remaining plurality of determined numeric thumbnails.