Document Heading Detection Using Semantic and Layout Vectors
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Solution Overview
Problem
Conventional table of contents (TOC) generation methods assume a known hierarchical structure and fail when dealing with documents without embedded printed TOCs or varying hierarchical structures.
Innovation Solution
A method that converts computer documents into markup language files, detects headings by analyzing both text content and layout using trained neural networks, and classifies them into pre-defined levels, enhancing heading detection and classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional TOC generation methods assume a known hierarchical structure and detect headings based on pre-defined entries, then the process is simple and fast, but it fails when dealing with documents that have varying hierarchical structures or no embedded printed TOC
Solution Approach 1:
The system dynamically adapts to different document structures by using trained neural networks that learn hierarchical patterns from training data. The neural networks can handle varying document formats and structures without requiring pre-defined templates, making the system dynamic rather than static in its approach to TOC generation.
Solution Approach 2:
The system changes its analysis parameters by evaluating multiple features including text content, layout properties, font characteristics, and positional information. By adjusting and weighing different parameters dynamically based on what the neural network deems most important, the system can adapt to various document structures effectively.
2Measurement precision
If the system analyzes both text content and layout using trained neural networks to detect headings, then heading detection accuracy is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system segments the analysis into distinct components: text content analysis, layout analysis, and hierarchical structure analysis. Each aspect is processed separately by specialized neural networks, allowing for more efficient computation compared to analyzing all features simultaneously in a single complex model.
Solution Approach 2:
The system performs preliminary actions by first converting the document to a structured format and extracting relevant features before applying the neural networks. This preprocessing step organizes the data in a way that reduces the computational burden during the actual heading detection phase.
3Manufacturing precision
If the system uses multiple trained neural networks to classify headings into pre-defined levels, then classification precision is improved, but the device complexity increases
Solution Approach 1:
The classification system is segmented into multiple specialized neural networks, each trained to detect specific heading levels or aspects. This division allows each network to focus on a particular classification task, improving precision while making the overall system more manageable through modular architecture.
Solution Approach 2:
The neural networks are designed with universal applicability to handle different document types and hierarchical structures. The same classification framework can be applied across various document formats by adjusting the training data and parameters, rather than creating separate systems for each document type.
Data Source
AI summary
Generating a table of contents from a computer document is disclosed. The computer document is converted into a markup language, from which a list of grouped textblocks is generated. Headings are detected from among the list of grouped textblocks. For a grouped textblock, a first vector corresponding to a semantic representation of the grouped textblock and a second vector based on evaluation of pre-defined features in the grouped textblock are generated. Based on the first and second vectors, the grouped textblock is classified as a heading or a plain-text using a trained classifier.