PDF to HTML Conversion with ML Annotation
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Solution Overview
Problem
Existing methods for converting PDF files to HTML suffer from poor conversion fidelity, presentation, and searchability, often resulting in poorly tagged and presented documents.
Innovation Solution
A computer-implemented method that extracts various elements from PDF documents, including text, formatting styles, background graphs, positional information, and font family information, and uses a machine learning algorithm to annotate HTML code, ensuring high conversion fidelity and presentation consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If PDF files are converted to HTML using existing methods, then the conversion can be completed, but the conversion fidelity and presentation quality are poor
Solution Approach 1:
The patent segments the PDF conversion process into distinct components: extracting text content, extracting formatting information, extracting background graphics, and separately processing these elements before reassembling them into HTML. This segmentation allows each component to be optimized independently, improving conversion fidelity while managing complexity through modular processing steps.
Solution Approach 2:
The patent introduces an intermediary processing stage that extracts and analyzes formatting information from the PDF, including fonts, colors, and layout properties. This intermediary step acts as a mediator between the source PDF and the target HTML, preserving formatting fidelity by capturing styling data separately from content data and applying it systematically during HTML generation.
2Reliability
If PDF files are converted to HTML using existing methods, then the conversion can be completed, but the searchability and content continuity are poor
Solution Approach 1:
The patent performs preliminary extraction and analysis of the PDF structure, including text content, formatting properties, and logical document organization, before generating the HTML output. By preparing and structuring the data in advance with proper semantic tags and hierarchical organization, the conversion achieves high searchability without requiring time-consuming post-processing or manual tagging corrections.
3Stability of the object's composition
If existing conversion methods are used, then HTML output can be generated, but the presentation and layout consistency are poor
Solution Approach 1:
The patent introduces an intermediary processing stage that extracts and analyzes formatting information from the PDF, including fonts, colors, and layout properties. This intermediary step acts as a mediator between the source PDF and the target HTML, preserving formatting fidelity by capturing styling data separately from content data and applying it systematically during HTML generation.
Solution Approach 2:
The patent systematically extracts and preserves key formatting parameters from the PDF document, including font families, sizes, colors, and layout properties. By capturing these parameters in an intermediate representation and applying them consistently during HTML generation, the method maintains layout stability and visual fidelity without requiring complex real-time rendering transformations.
Data Source
AI summary
Computer implemented method for converting PDF documents into human readable and machine parsable HTML code. The method includes the use of a machine learning algorithm in order to automatically annotate the HTML code, said algorithm being trained with a set of manually annotated HTML code examples.