Document Metadata Extraction Using Spatial Context and GCN Attention
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Solution Overview
Problem
Existing data extraction methods from documents, particularly using OCR, are inefficient due to the need for specific and time-consuming rule-based natural language processing, lack of genericity, and inability to capture style and spatial information effectively.
Innovation Solution
A method utilizing multiple embeddings, including surrounding and style embeddings, combined with a Graph Convolution Network with Informative Attention (GCN-IA), and a domain-aware tokenizer, to enhance metadata extraction by capturing spatial and semantic context, and addressing the out-of-vocabulary problem through secondary vocabulary creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based natural language processing is used for metadata extraction, then extraction can be performed, but the process becomes time-consuming and repetitive due to the need to define large numbers of specific rules for each document type
Solution Approach 1:
The patent replaces manual rule-based natural language processing with a deep learning-based automated system. The model automatically learns extraction patterns from training data, eliminating the need for engineers to manually define numerous specific rules for each document type, thereby significantly reducing rule definition time while maintaining extraction accuracy.
Solution Approach 2:
The deep learning model performs self-learning and self-adaptation to different document types through training on annotated data. The system automatically adjusts its extraction behavior based on learned patterns, eliminating the need for manual rule creation and enabling the system to serve itself across various document formats without extensive reconfiguration.
2Loss of information
If traditional OCR and NLP methods are used, then text can be extracted, but style and spatial information are not captured effectively
Solution Approach 1:
The patent merges OCR, NLP, and visual processing components into a unified deep learning model. This integrated architecture processes text, style, and spatial information simultaneously through multiple embedding layers and attention mechanisms, effectively capturing all document attributes without requiring separate complex systems for each function.
Solution Approach 2:
The patent adds visual and spatial dimensions to traditional text extraction by incorporating style embeddings that capture font, color, and formatting information, as well as spatial embeddings that encode positional relationships. This multi-dimensional approach transforms the extraction process from purely textual to holistic document analysis.
3Reliability
If standard tokenization is used, then text processing can proceed, but out-of-vocabulary words cause extraction errors
Solution Approach 1:
The patent performs preliminary actions to address out-of-vocabulary issues by creating a comprehensive vocabulary during training that includes domain-specific terms and variations. The model also employs subword tokenization and embedding techniques that can represent unseen words by combining known subword units, preventing extraction errors before they occur.
Data Source
AI summary
A method of extracting meta-data from a document includes capturing style attributes from the document, identifying cell-wise location coordinates for text characters using page segmentation and border table extraction, and finding relationship between nearby cells using surrounding embedding by determining shortest distant text cell in top, left, right, and bottom direction. The method further includes applying Graph Convolution Network with Informative Attention (GCN-IA) for providing more attention to informative nodes for generating better representation of surrounding embedding and capturing a deep contextual meaning from text cells. A domain specific language model is utilized and improved by a domain aware tokenizer. The method includes capturing a complex visual layout of the document using the domain specific visual model, determining meta-data information, representing linguistic and visual contexts of the document, and correcting the extracted output by applying advanced-post processing on the extracted output from advanced language-visual model.