Emphasized Text Identification via Density Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image processing technologies fail to efficiently identify and differentiate emphasized text in scanned documents, leading to reduced character recognition accuracy and increased computational resources, especially when dealing with mixed text styles or Asian language texts.
Innovation Solution
The method involves horizontal compression and morphological dilation of the input image to form clusters, followed by calculating bounding boxes and average densities, allowing for the identification of emphasized text by comparing local densities within these boxes, which helps in distinguishing bold or underlined text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If character recognition process processes all text in the document, then complete text recognition is achieved, but computing resources are wasted and processing time increases
Solution Approach 1:
The patent segments the text processing task by first identifying emphasized text regions (through compression and density analysis) and then applying character recognition only to these segmented regions. This divides the full document into emphasized and non-emphasized portions, processing only the relevant segments to improve efficiency while maintaining accuracy for important content.
Solution Approach 2:
The patent applies different processing quality levels to different parts of the document. Emphasized text regions receive full character recognition processing, while non-emphasized regions are skipped or given minimal processing. This local differentiation optimizes resources by concentrating computational effort where it matters most for document understanding.
2Device complexity
If standard character recognition is applied to all text, then uniform processing is simple, but recognition accuracy deteriorates for emphasized text
Solution Approach 1:
The patent performs preliminary action by identifying and tagging emphasized text regions before applying character recognition. Through horizontal compression and density comparison, the system pre-markes which text regions contain emphasis (bold, underline, italic), allowing the subsequent recognition process to apply appropriate algorithms to each region type, thereby improving accuracy without excessive complexity.
3Productivity
If no text style identification is performed, then processing is faster, but important information is lost
Solution Approach 1:
The patent extracts emphasis information from the document by analyzing local density patterns in compressed representations. By taking out and identifying the emphasis attributes (bold, underline, italic) through density comparison, the system preserves this important information separately, allowing fast processing of the main text while retaining stylistic information for later use in understanding document structure and importance.
Data Source
AI summary
To identify emphasized text, bounding boxes are based on clusters resulting from horizontal compression and horizontal morphological dilation. The bounding boxes are processed to determine if any contain words or characters in bold. A bounding box is eliminated based on a comparison of its density and an average density across all bounding boxes. If its density is greater, text elements within the bounding box are evaluated to determine whether the text element is bold.