Text Header Classification Using Group and Font Characteristics

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Solution Overview

Problem

Existing systems struggle to accurately classify and format headers in electronic documents due to the lack of effective methods for identifying and tagging text strings as headers based on both group and font characteristics.

Innovation Solution

A system that partitions a data corpus into text strings and applies header tags based on group and font characteristic criteria, using a header management system to classify text strings as headers and apply appropriate tags based on font prominence.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If header tags are applied to text strings in electronic documents, then the structure and readability of the content is improved, but the complexity of identifying and classifying headers increases

Engineering Contradiction:

Improveheader classification accuracyVSAvoidheader identification system complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The patent segments the header identification process into distinct evaluation stages: first evaluating group characteristics (positioning, spacing, repetition patterns), then evaluating font characteristics (size, weight, style) only for text strings that pass the group characteristics filter. This segmentation reduces overall system complexity by breaking down the classification task into manageable, sequential steps.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies different evaluation criteria and thresholds to different positions and contexts within the document. Group characteristics criteria vary based on the text string's position (e.g., top of page, section breaks), and font characteristics are evaluated with position-specific thresholds. This localized approach improves classification accuracy without requiring a single complex universal rule set.

Inventive Principle:

Principle #3Local quality

2Measurement precision

If multiple criteria (group and font characteristics) are evaluated for header classification, then the precision of header identification is improved, but the time and computational resources required increase

Engineering Contradiction:

Improveheader identification precisionVSAvoidheader classification processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent performs preliminary evaluation of group characteristics (positioning, spacing, repetition) before conducting the more computationally intensive font characteristic analysis. Text strings that fail to meet group characteristics criteria are disqualified early, preventing unnecessary font analysis and reducing overall processing time while maintaining high identification precision through the two-stage filter approach.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent applies font characteristic evaluation selectively only to text strings that have already passed the group characteristics filter, rather than evaluating all text strings in the document. This partial application of the more resource-intensive font analysis reduces computational overhead while maintaining identification precision for relevant candidates.

Inventive Principle:

Principle #16Partial or excessive action

3Manufacturing precision

If font prominence criteria are used to determine header tags, then the formatting accuracy of headers is improved, but the difficulty of detecting and measuring font characteristics increases

Engineering Contradiction:

Improveheader formatting accuracyVSAvoidfont characteristic detection difficulty

Core Design Contradiction:

Manufacturing precisionVSDifficulty of detecting and measuring

Solution Approach 1:

The patent introduces group characteristics (positioning, spacing, repetition patterns) as an intermediary filter between the raw text and the final header classification. This intermediary layer reduces the number of text strings requiring detailed font characteristic measurement, making the detection and measurement process more manageable while maintaining formatting accuracy through the combined evaluation of both group and font characteristics.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12412023B2Identifying and formatting headers for text content

Publication Date: 2025.09.09 ORACLE INT CORP

AI summary

A data corpus is partitioned into text strings for header classification. A group characteristic is computed for a text string, and whether the group characteristic satisfies a group characteristic criterion is determined. The text string may be disqualified from header classification if the group characteristic criterion is not satisfied, or one or more font characteristics may be determined for the text string if the group characteristic criterion is satisfied. A font characteristic that meets one or more prevalence criteria may be identified and evaluated to determine whether the font characteristic meets at least one font characteristic criterion. The text string may be disqualified from header classification if the font characteristic criterion is not satisfied, or if the font characteristic meets the font characteristic criterion, the text string is classified as a header, and tagged content is generated by applying a header tag to the text string.