Document Image Natural Image Isolation via Connected Component Segmentation
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Solution Overview
Problem
Document scanners and camera devices capture images with image noise, making it challenging to distinguish natural images from background noise in document images, especially in complex documents with varying text sizes, font styles, and embedded content.
Innovation Solution
An image processing method that generates combined connected components, identifies candidate text, background, and natural image components, and modifies the document image by expanding natural image bounding boxes to include intersecting components, thereby isolating and protecting natural images from noise reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If noise reduction processing is applied to document images, then background noise is reduced, but natural images are degraded or destroyed
Solution Approach 1:
The document image is segmented into multiple connected components (CCs) based on pixel intensity and spatial connectivity. These CCs are then classified into different categories (text, natural image, background) using multiple criteria including aspect ratio, fill density, and spatial distribution. This segmentation allows selective noise reduction application - background regions undergo noise reduction while natural image regions are preserved.
Solution Approach 2:
Different processing qualities are applied to different regions of the document image. Background regions receive aggressive noise reduction processing, while regions identified as natural images receive protective processing to maintain their original quality. The system dynamically adjusts processing intensity based on local region classification, ensuring that natural images are not degraded by uniform noise reduction applied across the entire document.
2Measurement precision
If connected component analysis is used to identify natural images, then natural image detection accuracy improves, but processing complexity increases
Solution Approach 1:
The image processing is divided into sequential stages: first generating connected components from the document image, then classifying these CCs using multiple independent criteria (aspect ratio, fill density, spatial distribution), and finally synthesizing these classifications to identify natural images. This staged segmentation of the analysis process improves detection accuracy while keeping each individual processing step relatively simple and computationally efficient.
Solution Approach 2:
The connected component analysis framework serves multiple functions simultaneously: it identifies natural images, distinguishes them from text regions, determines background areas, and provides the basis for selective noise reduction. By making the CC analysis multi-functional, the system achieves high detection accuracy without adding separate processing pipelines, thereby limiting the increase in overall processing complexity.
Data Source
AI summary
An image processing method includes: generating, from combined connected components (CCs) of a document image, candidate text CCs, candidate background CCs, and candidate natural image CCs where the candidate background CCs are excluded from the combined CCs to generate the candidate natural image CCs with a predetermined criterion dependent on the candidate text CCs; generating a final natural image bounding box by expanding a candidate natural image bounding box of the candidate natural image CCs and including in the expanded candidate natural image bounding box at least one combined CC that intersects the expanded candidate natural image bounding box; and modifying, based on the final natural image bounding box, the document image and displaying the modified document image to a user.