Neural Network Entity Detection via Widget-Text Pairing
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Solution Overview
Problem
Conventional entity detection in documents is cumbersome, time-consuming, and requires extensive human resources due to the need for manual processing, which lacks efficiency and accuracy when automated systems replace human intelligence.
Innovation Solution
A method and system for training a neural network to detect entities in documents by processing documents through detection and extraction modules, classifying widget entities, generating training pairs, and employing convolutional layers to derive relevant inferences, thereby automating the detection process and improving efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processing is used for entity detection, then accuracy and understanding of context are improved, but time consumption and human resource requirements increase
Solution Approach 1:
The system performs preliminary processing by converting documents to gray scale, applying thresholding to create binary images, and detecting edges before entity detection. These preparatory steps automatically process documents in advance, reducing the need for time-consuming manual analysis while maintaining detection accuracy through structured preprocessing pipelines.
Solution Approach 2:
The patent replaces manual mechanical processing with automated image processing algorithms. Convolutional neural networks and morphological operations automatically detect widget entities, checkboxes, and text regions, substituting human intelligence with machine-based pattern recognition that operates faster without sacrificing accuracy.
2Productivity
If automated detection techniques are used, then efficiency and productivity are improved, but understanding of context and derivation of relevant inferences deteriorate
Solution Approach 1:
The system enhances automated detection by adding multiple processing dimensions: image processing for visual pattern recognition, text extraction for contextual analysis, and neural network classification for semantic understanding. This multi-dimensional approach allows automated systems to maintain context understanding while achieving high detection efficiency across different data types simultaneously.
Solution Approach 2:
The patent employs a composite detection system combining multiple algorithms and processing techniques. By integrating image processing, OCR, neural networks, and rule-based extraction methods, the system creates a robust automated framework that preserves contextual information while maintaining high productivity through parallel processing capabilities.
3Loss of information
If manual entity detection is performed, then relevant inferences and patterns are accurately derived, but device complexity and operational requirements increase
Solution Approach 1:
The patent merges multiple detection functions into a unified automated system. The neural network model integrates widget entity detection, checkbox state recognition, and text extraction into a single processing pipeline, reducing operational complexity while maintaining inference accuracy through consolidated architecture that handles multiple tasks simultaneously.
Solution Approach 2:
The system implements self-service capabilities where the neural network automatically learns patterns and derives inferences from training data without manual intervention. The model performs self-training on labeled datasets, automatically adjusting parameters and improving inference accuracy while reducing the complexity of manual system configuration and operation.
Data Source
AI summary
A system and method for training a neural network is implemented for detecting at least one entity in a document to derive relevant inferences therefrom. The method describes obtaining at least one document. The at least one document is processed, via a detection module, to detect a widget entity. The detected widget entity is classified as active or inactive based on a detected state of the widget entity. The classified widget entity is modified into a corresponding machine-readable widget-entity based on the detected state. The at least one document is processed, via an extraction module, to detect a text entity in near vicinity of the classified widget entity. A training pair comprising the machine-readable widget entity and the corresponding text entity is generated. The neural network is trained using the generated training pair.