Neural Network Block Detection in Complex Documents
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Solution Overview
Problem
Conventional methods for detecting blocks of associated words or symbol sequences in electronic documents with complex structures are inefficient, relying heavily on manual heuristics and struggling with accurate recognition, especially when fields and blocks are not physically close or are located on multiple pages.
Innovation Solution
The use of neural networks to automatically detect blocks of associated symbol sequences by processing vectors representative of symbol sequences, recalculating these vectors based on their values, and determining association values to identify connected sequences, thereby improving detection accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional manual heuristic methods are used for detecting blocks of associated words, then the system can handle complex document structures, but the detection accuracy deteriorates when fields and blocks are not physically close or are located on multiple pages
Solution Approach 1:
The patent introduces neural networks as an intermediary between the input document data and the block detection output. The neural network processes vector representations of symbol sequences and learns to identify associations between elements regardless of their physical proximity or document structure complexity, thereby maintaining high detection accuracy across diverse document formats without relying on manual heuristics
2Device complexity
If manual heuristic methods are used for block detection, then the system can operate with simple processing, but the processing speed and efficiency deteriorate due to the large number of manual operations required
Solution Approach 1:
The patent replaces manual heuristic processing with an automated neural network system. The neural network automatically processes vector representations of symbol sequences, recalculates vectors based on their values, and determines association values to identify blocks of associated words. This substitution of mechanical manual operations with an automated intelligent system dramatically improves processing speed and efficiency while maintaining simplicity in operation
3Measurement precision
If neural networks are used to automatically detect blocks of associated symbol sequences, then detection accuracy improves, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the block detection task into distinct processing stages: obtaining symbol sequences from the document, determining vector representations of these sequences, processing the vectors through neural networks to obtain recalculated vectors, determining association values between sequences, and finally identifying blocks of associated symbol sequences. This segmentation of the complex detection process into manageable stages reduces overall computational complexity while maintaining high detection accuracy
Data Source
AI summary
Aspects of the disclosure provide for mechanisms for identification of blocks of associated words in documents using neural networks. A method of the disclosure includes obtaining a plurality of words of a document, the document having a first block of associated words, determining a plurality of vectors representative of the plurality of words, processing the plurality of vectors using a first neural network to obtain a plurality of recalculated vectors having values based on the plurality of vectors, determining a plurality of association values corresponding to a connections between at least two words of the document, and identifying, using the plurality of recalculated vectors and the plurality of association values, the first block of associated symbol sequences.