Background Pixel Segmentation for Variable Document Headers
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Solution Overview
Problem
Existing machine learning models struggle to accurately identify and group related content within document headers due to varying formats and characteristics among different suppliers, such as font types, spacing, and text box locations, making it difficult to classify relevant data effectively.
Innovation Solution
A system that clusters background regions in an electronic document by analyzing rows and columns of pixels, generating a binary representation, and identifying significant non-content groups to determine foreground regions without requiring semantic content analysis, using threshold-based classification and normal distribution for accurate boundary identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine learning models analyze text attributes to group content, then they can identify related text, but they fail to accurately group content in document headers due to varying formats and characteristics
Solution Approach 1:
Instead of directly analyzing and grouping foreground content regions, the patent inverts the approach by identifying and grouping background regions first, then deriving foreground regions as the complement. This inversion allows the system to handle format variability more effectively by focusing on the consistent characteristics of background areas rather than the diverse foreground content.
Solution Approach 2:
The patent segments the document processing task into distinct phases: background pixel identification, background region clustering, and foreground region derivation. By dividing the problem into these manageable segments, the system can apply specialized techniques to each phase, improving overall accuracy in content grouping despite format variations.
2Measurement precision
If the system analyzes semantic content to identify foreground regions, then it can classify data accurately, but it requires complex semantic analysis that increases processing complexity
Solution Approach 1:
The patent extracts the background regions from the document and performs clustering analysis on them separately. By taking out the background analysis as a distinct step, the system avoids the complexity of semantic analysis while still achieving accurate foreground region identification through the complement approach.
Solution Approach 2:
The patent introduces background region clustering as an intermediary step between raw pixel data and foreground region identification. This intermediary process simplifies the overall task by transforming the complex problem of foreground detection into a more manageable background clustering problem, reducing processing complexity.
3Measurement precision
If the system uses traditional content analysis methods, then it can identify text regions, but it cannot effectively distinguish background areas in documents with diverse formats
Solution Approach 1:
The patent applies inversion by focusing on background regions rather than foreground content. Since background areas tend to have more consistent characteristics across diverse document formats compared to the variable foreground content, this approach enables effective background distinction while maintaining adaptability to format diversity.
Solution Approach 2:
The patent changes the analysis parameters from content-based semantic attributes to pixel-based visual characteristics for background identification. By switching to parameters like pixel intensity, color, and spatial distribution, the system can effectively distinguish background regions across documents with diverse formats and content types.
Data Source
AI summary
Techniques for segmenting an electronic document based on identifying background regions within the document are disclosed. A system identifies foreground regions—such as regions containing text and graphics—in an electronic document by clustering background regions of the document to determine the boundaries of the remaining regions. The system identifies the remaining regions as foreground regions, without requiring an analysis of distances between the foreground regions and without requiring an analysis of the semantic content in these regions. The system clusters the background regions by identifying horizontal slices of content (content slices) and non-content slices, generating a binary representation of the content slices, and clustering background regions within each content slice based on the binary representations.