PDF Table Structure Recovery Using Sequence Tagging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for extracting structural information from PDF documents, such as tables, are cumbersome and fragile, especially when dealing with non-English documents, as they rely on heuristic rules that fail to account for diverse table structures and lack explicit machine-readable formats.
Innovation Solution
A system and method using sequence tagging and conditional random fields to identify table structures in native PDF documents by detecting physical features with weighting factors, updating these factors based on known locations, and extracting rows, columns, and cells, enabling accurate structure recovery across languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If heuristic applications are used for structure recovery of PDF tables, then high quality output can be produced for certain types of input, but the applications become extremely cumbersome and fragile for diverse inputs
Solution Approach 1:
The patent replaces heuristic rule-based mechanical processing with a machine learning model that automatically learns patterns from training data. The ML model substitutes the complex, fragile heuristic applications with a more robust statistical approach that handles diverse inputs without becoming cumbersome.
Solution Approach 2:
The system changes parameters by training the machine learning model on multiple features of PDF documents (font characteristics, spacing, alignment, etc.) with associated weighting factors. This allows the system to adapt to different table structures by adjusting the importance of various physical features rather than relying on fixed heuristic rules.
2Adaptability or versatility
If heuristic rules are used to identify structures for English documents, then structure recovery can be achieved, but the rules become useless for non-English PDF documents
Solution Approach 1:
The patent creates a universal machine learning model that can process multiple languages by training on diverse multilingual data. The model learns language-agnostic structural patterns in PDF tables, making it applicable to English, Chinese, and other languages without requiring language-specific heuristic rules.
Solution Approach 2:
The system performs preliminary training action by pre-training the machine learning model on a large corpus of multilingual PDF documents with known table structures. This preliminary learning phase enables the model to generalize across languages and maintain reliability when processing documents in languages it hasn't explicitly seen during inference.
3Ease of operation
If PDF documents are converted to HTML, then the documents can be processed by web applications, but table structures are lost and only plain text is displayed
Solution Approach 1:
The patent applies segmentation by identifying and separating table structures from the rest of the PDF document content before conversion. The machine learning model detects table boundaries, rows, and columns, allowing the structure to be preserved in the converted HTML format while maintaining ease of web application processing.
Solution Approach 2:
The system introduces an intermediary processing step between PDF and HTML conversion. The machine learning model acts as a mediator that analyzes the PDF structure, identifies tables and their components, and generates appropriate HTML markup that preserves the original structure while enabling web compatibility.
4Measurement precision
If structural information is extracted from PDF documents, then data extraction and translation quality improve, but the extraction process becomes challenging due to lack of explicit machine-readable formats
Solution Approach 1:
The patent replaces difficult manual or rule-based detection of structural information with automated machine learning detection. The model learns to identify tables, paragraphs, and other structural elements by analyzing visual and textual patterns, making the extraction process more accurate and less challenging.
Solution Approach 2:
The system implements feedback by using detected structural information to improve subsequent extraction operations. The machine learning model learns from detected patterns and adjusts its detection strategy, creating a feedback loop that continuously improves extraction accuracy while reducing the difficulty of detecting structural elements.
Data Source
AI summary
There is provided a method to identify structure of a native PDF document. The method comprises: obtaining a native PDF document having a first line to start a table and a second line to end the table; detecting a value of a physical feature of the native PDF document, wherein the physical feature has a corresponding weighting factor; initiating a value to the weighting factor; assigning a first status for the first line and a second status for the second line based on (a) the physical feature and (b) the weighting factor; and identifying a location of the table on native PDF document from the first status and the second status, thus yielding an identified location of the table.