AI Table Region Identification in PDF Documents
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Solution Overview
Problem
Existing document processing techniques fail to accurately identify and extract tables from PDF documents due to their diversity in size, structure, alignment, and layout, often resulting in incorrect extraction and lack of table-specific markup in the PDF format.
Innovation Solution
A system utilizing artificial intelligence and machine learning, specifically an artificial neural network (ANN), is employed for table region identification, clustering, and label correction within discretized contiguous areas of PDF documents, enabling precise extraction and annotation of tables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing document processing techniques are used to extract tables from PDF documents, then the processing speed is maintained, but the extraction accuracy deteriorates due to table diversity in size, structure, alignment, and layout
Solution Approach 1:
The patent segments the table extraction task into multiple discrete steps: table region detection, structure analysis, cell identification, and content extraction. This segmentation allows the system to handle diverse table formats by processing each aspect separately with specialized algorithms, thereby improving both accuracy and adaptability to different table structures
Solution Approach 2:
The patent employs parameter changes by adjusting extraction thresholds, confidence levels, and processing parameters based on the detected table characteristics. The system dynamically modifies parameters such as table boundary detection sensitivity and cell merging criteria to adapt to different table formats, improving extraction accuracy across diverse table types
2Measurement precision
If manual annotation is used to correct table extraction errors, then the extraction accuracy is improved, but the time consumption and labor costs increase
Solution Approach 1:
The patent implements a feedback mechanism where extracted tables are automatically evaluated against confidence thresholds, and only low-confidence extractions are flagged for manual review. This feedback loop enables the system to learn from correction patterns and automatically improve future extractions, reducing the proportion of documents requiring manual annotation over time while maintaining high accuracy
Solution Approach 2:
The patent applies partial action by performing automatic extraction for high-confidence cases and reserving manual annotation only for low-confidence or ambiguous cases. This selective approach achieves high overall accuracy while minimizing the time and resources required for manual intervention
3Measurement precision
If comprehensive table structure analysis is performed to handle diverse table formats, then the extraction accuracy is improved, but the processing complexity increases
Solution Approach 1:
The patent employs dynamic processing where the level of analysis depth and processing intensity are adjusted based on the detected table characteristics. The system dynamically selects appropriate processing strategies for different table types, avoiding unnecessary complex analysis for simple tables while applying comprehensive analysis only when needed, thereby managing processing complexity while maintaining accuracy
4Measurement precision
If table-specific markup is added to PDF format to improve extraction accuracy, then the extraction precision is improved, but the PDF format compatibility and ease of operation deteriorate
Solution Approach 1:
The patent introduces an intermediary layer that translates diverse PDF table formats into a standardized internal representation for processing, then converts the extracted results into the desired output format. This intermediary approach enables accurate extraction without requiring modifications to the original PDF format or requiring special markup, maintaining PDF compatibility and ease of operation while achieving high extraction precision
Data Source
AI summary
A computer system, product, and method are provided to utilize machine learning to facilitate document processing. A document collection is introduced to an artificial neural network (ANN), which subjects the document collection to table region identification within discretized contiguous areas. The documents are assigned to one or more clusters responsive to the leveraged ANN. Documents are selectively evaluated from the clusters, and one or more label corrections are applied to the ANN. The ANN generates an updated document collection incorporating the applied one or more label corrections.