Tabular Data Extraction via Hierarchical Feature Segmentation
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Solution Overview
Problem
Existing techniques for detecting and extracting tabular data from documents are limited in effectiveness, robustness, and efficiency due to their inability to handle the vast variability in formats and structures, including different row and column heights, merged cells, varying borders and colors, nested tables, and hierarchical arrangements.
Innovation Solution
A method and system that generate a hierarchy of features based on relative spatial properties of image document features, segment the document into semantic segments, and classify these segments into tabular structures to detect and extract tabular data, employing machine learning models for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing techniques use partitioning, clustering, or pre-developed rules for tabular data extraction, then the process can be implemented with simple methods, but the effectiveness and robustness are limited due to inability to handle vast variation in table formats and structures
Solution Approach 1:
The document is segmented into multiple semantic segments based on hierarchical features. Each segment is then independently classified to identify tabular structures. This segmentation allows the system to handle complex and varied table formats by processing smaller, manageable portions of the document separately.
Solution Approach 2:
The system transitions from traditional 2D image processing to a hierarchical feature representation that adds multiple levels of abstraction. By organizing features into a hierarchy (e.g., pixel-level features, word-level features, sentence-level features), the system can capture both local and global structural information, enabling it to handle diverse table formats effectively.
2Adaptability or versatility
If existing techniques rely on specific rules or scoring techniques for table detection, then the implementation can be simpler, but the ability to handle complex table structures like nested tables and multi-level headers is limited
Solution Approach 1:
The hierarchical feature classification system serves multiple functions simultaneously: it detects tables, identifies nested tables, recognizes multi-level headers, and extracts tabular data all through the same unified approach. The system uses a single classification mechanism that can handle various table configurations without requiring separate specialized algorithms for each case.
Solution Approach 2:
The system performs preliminary segmentation and hierarchical feature extraction before the actual table detection and extraction. By pre-organizing the document into semantic segments and computing hierarchical features in advance, the system prepares the data structure needed for efficient classification and subsequent tabular data extraction, reducing the complexity of the main processing task.
Data Source
AI summary
This disclosure relates generally to document processing, and more particularly to method and system for detecting and extracting tabular data from a document. In one embodiment, the method may include generating a hierarchy of features, for a plurality of features of an image document derived from the document, based on relative spatial properties of the plurality of features. The method may further include segmenting the image document into a plurality of semantic segments based on the hierarchy of features, classifying each of the plurality of semantic segments into at least one of a plurality of tabular structures, and effecting at least one of a detection or an extraction of the tabular data from the image document based on the classification.