Dynamic Character Prominence Adjustment for Text Legibility
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current word processing and typesetting technologies face challenges in optimizing text composition to balance legibility, readability, and print economy, often compromising on uniformity and aesthetic appeal due to fixed line lengths and margins, and struggle to dynamically adjust character prominence based on informative content.
Innovation Solution
A computer-implemented system and plug-in application that analyzes text input by assigning information measures to characters, adjusting their physical features such as size, spacing, and contrast to enhance the prominence of informative characters and reduce the prominence of less informative ones, while maintaining the text's format and satisfying user-defined aesthetic constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text density is increased to improve legibility, then reading speed improves, but uniformity and aesthetic appeal deteriorate
Solution Approach 1:
The patent applies different text density levels to different regions of the text based on information measure calculations. High-information characters receive enhanced prominence through increased density, while low-information characters maintain normal or reduced density, creating local variations that preserve overall uniformity while improving legibility where it matters most.
Solution Approach 2:
The system dynamically adjusts text density as a variable parameter based on the information measure of characters. Rather than using fixed density throughout, the patent modifies density values selectively to emphasize important characters, thereby improving legibility without sacrificing the overall uniform appearance of the text block.
2Measurement precision
If character prominence is adjusted dynamically based on information content, then readability improves, but device complexity increases
Solution Approach 1:
The patent calculates information measures for all characters in advance, before the actual text composition and rendering process. This preliminary analysis allows the system to prepare prominence adjustment values ahead of time, reducing the computational burden during text generation and simplifying the real-time processing requirements.
Solution Approach 2:
The system divides the text processing into separate stages: information measure calculation, prominence value determination, and actual text composition. By segmenting the complex task of dynamic prominence adjustment into manageable components, the patent reduces overall system complexity while maintaining improved readability.
3Stability of the object's composition
If fixed line lengths and margins are used to maintain format consistency, then layout stability improves, but adaptability to informative content deteriorates
Solution Approach 1:
The patent maintains fixed line lengths and margins for overall layout stability, while introducing local variations in character prominence within those constraints. Important characters are enhanced through increased density or size adjustments, allowing the format to remain stable while adapting to the information content locally.
Solution Approach 2:
Within the static framework of fixed line lengths and margins, the system introduces dynamic character-level adjustments based on information measures. This allows the text to adapt its visual presentation to highlight important content while maintaining the structural integrity and consistency of the overall layout.
Data Source
AI summary
A computer implemented system and method for composing a formatted text input to improve legibility, readability and/or print economy while preserving the format of the text input and satisfying any user selected aesthetic constraints. An information measure (IM) is assigned to each character in a language unit. Multiple different IMs are assigned to each character and combined to form a combined IM (CIM) for each character indicating the predictability of that character to differentiate the language unit from other language units. The process is repeated for at least a plurality of language units and typically until all the text input has been analyzed and information measures assigned to all of the characters.