Neural Network Word Clustering for Document Text Realignment
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Solution Overview
Problem
Conventional neural networks face limitations in handling diverse inputs due to the need for predefined classes and attributes, which restrict their utility and require extensive training, making them inefficient for classifying inputs without prior knowledge of the classes or attributes.
Innovation Solution
The neural clustering system (NCS) employs a neural network trained for clustering, allowing it to group inputs based on discovered characteristics without pre-defined classes, using supervised clustering to identify and realign text in documents with distortion, and utilizing an adjacency matrix to determine similarity between inputs for clustering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional neural networks use pre-defined classes for classification, then classification accuracy is improved, but adaptability to new input types deteriorates
Solution Approach 1:
The patent inverts the conventional classification approach by replacing pre-defined classes with dynamically generated clusters. Instead of forcing inputs into predetermined categories, the system allows clusters to emerge organically from the data through neural network processing, enabling the model to adapt to any input type without prior class definitions while maintaining grouping accuracy
Solution Approach 2:
The patent implements dynamic clustering where cluster structures are not fixed but adapt continuously based on input characteristics. The neural network generates clusters on-the-fly based on similarity metrics, allowing the system to dynamically adjust to new types of inputs without retraining, thus resolving the contradiction between accuracy and adaptability
2Device complexity
If pre-defined classes are used for neural network classification, then classification structure is simplified, but training time and resource requirements increase
Solution Approach 1:
The patent enables the neural network to automatically generate its own clustering structure without requiring external definition of classes or attributes. The system serves itself by autonomously identifying patterns and creating clusters based on input similarity, eliminating the time-consuming process of manual class definition and reducing training requirements while maintaining structured organization
3Stability of the object's composition
If rigid class definitions are imposed on neural network inputs, then classification consistency is improved, but handling of diverse input types deteriorates
Solution Approach 1:
The patent changes the fundamental parameter from fixed class labels to dynamic similarity-based clustering. By using similarity metrics and distance measures instead of rigid class definitions, the system maintains consistency through systematic grouping while simultaneously accommodating diverse input types, as clusters form based on actual data characteristics rather than predetermined categories
Data Source
AI summary
Various embodiments for a neural network clustering system are described herein. An embodiment operates by detecting a plurality of bounding boxes and identifying coordinates for each of the bounding boxes. An adjacency matrix is generated based on combining a key matrix and a query matrix. The plurality of words are clustered into a plurality of clusters, each cluster corresponding to a different line on the first document. A second document is generated in which the plurality of words corresponding to a respective cluster of the plurality of clusters is arranged on a same line on the second document. The second document is provided for display.