GUI for Hierarchical Data Extraction Training
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Solution Overview
Problem
Existing machine learning systems lose substantial hierarchical data when converting written documents to plain text, leading to inefficient parsing and requiring extensive labeled training data for semi-structured documents.
Innovation Solution
A Graphical User Interface (GUI) system with learning capabilities that detects and extracts hierarchical groups, links, and labels from written documents, allowing for automated feature extraction and reduced labeled training data requirements by using boundary markers and hierarchical links to train a layout data analysis model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If machine learning systems convert written documents to plain text as a first step, then text processing becomes simpler, but substantial hierarchical data is lost
Solution Approach 1:
The system segments the document processing into distinct hierarchical levels (page, section, paragraph, sentence, word) rather than treating all text uniformly. Each hierarchical group is identified and processed separately, preserving the structural information that would be lost in plain text conversion while enabling targeted processing at each level.
Solution Approach 2:
The system adds a hierarchical dimension to traditional text processing by introducing spatial and structural coordinates (bounding boxes, hierarchical levels, parent-child relationships) alongside the textual content. This transforms the problem from 1D text sequence to 2D/3D structured data that retains both content and layout information.
2Loss of information
If hierarchical data extraction is performed manually to preserve document structure, then information quality improves, but time consumption increases significantly
Solution Approach 1:
The system enables semi-automated extraction where the machine learning model performs the bulk of hierarchical identification work, and human operators only intervene to correct errors or handle ambiguous cases. This self-service approach maintains high information quality while dramatically reducing the time investment compared to fully manual extraction.
Solution Approach 2:
The system incorporates feedback loops where model predictions are evaluated against ground truth or user corrections, and this feedback is used to iteratively improve the extraction accuracy. This allows the system to learn from errors and continuously enhance performance without requiring complete manual re-annotation.
3Measurement precision
If extensive labeled training data is used to train machine learning models for document parsing, then parsing accuracy improves, but data preparation effort increases
Solution Approach 1:
The system performs preliminary hierarchical group identification and boundary detection before the main parsing task. By pre-processing documents to identify structural elements and create initial annotations, the system reduces the amount of manually labeled training data needed for subsequent model training, as the preliminary structure provides a strong foundation for learning.
Data Source
AI summary
A system comprising: an input receiving an input document comprising text data and graphical data distinguishing hierarchically first and second portion of the text data; a display displaying said input document; a user interface allowing a user to add, in superposition with the displayed input document, boundary markers visually bounding said first and second text portions; and a processor arranged for, using the boundary markers added to the displayed input document, training a layout data analysis model to determine, in a further input document having further text data, if graphical data distinguishes hierarchically first and second portions of the further text data to display automatically boundary markers visually bounding said first and second portions of said further text data; the user interface allowing said user to correct the boundary markers displayed by the layout analysis model and the processor training the layout data analysis model using the corrected boundary markers.