Document Segmentation Using Homogeneity and Projection Profiles
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Solution Overview
Problem
Existing document segmentation methods face challenges in accurately distinguishing text and non-text components, especially in complex layouts with varying font sizes and rich non-text components, due to the lack of reliable text and non-text classification techniques.
Innovation Solution
A computer-implemented method that uses proximity, similarity, and regularity characteristics to differentiate text regions from non-text regions by analyzing morphological and texture features of connected components, employing a probabilistic cue integration based on image characteristic parameters and grey-level co-occurrence matrices to determine homogeneity levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bottom-up methods are used for document segmentation, then local neighborhood information is exploited effectively, but the methods are time consuming and fail to discriminate text from non-text components
Solution Approach 1:
The patent applies preliminary action by computing projection profiles (horizontal and vertical projections of pixel intensities) before the main segmentation process. These projection profiles are pre-computed to capture global structural information, enabling faster text-non-text discrimination during the actual segmentation phase without sacrificing accuracy.
Solution Approach 2:
The patent segments the document image into multiple regions using both bottom-up (connected component analysis) and top-down (projection profile-based) approaches. By dividing the segmentation task into multiple stages with different strategies, the method achieves both local detail preservation and global structural awareness, improving discrimination accuracy while managing computational complexity.
2Adaptability or versatility
If top-down methods are used for document segmentation, then global information is utilized for iterative segmentation, but the methods suffer from non-Manhattan layouts and lack reliable text-non-text classification
Solution Approach 1:
The patent merges bottom-up and top-down methods into a hybrid segmentation framework. The bottom-up approach (connected component analysis) captures local textual structures, while the top-down approach (projection profiles) captures global layout patterns. By combining both approaches, the system achieves versatility in handling diverse layouts while maintaining reliable text-non-text classification through multiple complementary cues.
Solution Approach 2:
The patent implements feedback mechanisms where projection profiles are continuously refined based on segmented region information, and segmentation results are adjusted based on projection profile analysis. This iterative feedback loop allows the system to adapt to various layout configurations while improving classification accuracy through repeated refinement of both global and local features.
3Ease of manufacture
If heuristics or simple statistics are used for text-non-text classification, then implementation is straightforward, but performance suffers on complicated documents with versatile layouts
Solution Approach 1:
The patent uses composite materials analogy by combining multiple types of features (projection profiles, connected component properties, morphological features, texture features) into a composite classification criterion. This composite approach maintains relative implementation simplicity while significantly improving classification performance on complex documents with versatile layouts, as the multiple features complement each other to capture diverse document characteristics.
Solution Approach 2:
The patent creates a universal classification framework that handles both simple and complex documents through a unified multi-feature approach. The same classification mechanism (combining projection profiles with connected component analysis) works across diverse document types, from simple text documents to complex layouts with figures, tables, and mixed content, eliminating the need for document-type-specific processing.
4Measurement precision
If learning based approaches are used for text-non-text classification, then performance on complex documents improves, but sufficient training samples are required which are expensive to collect
Solution Approach 1:
The patent applies self-service by enabling the system to automatically extract and utilize features directly from the document images without requiring external training data collection. The projection profiles and connected component features are computed directly from the input images, allowing the classification system to adapt to complex document layouts through self-contained feature extraction rather than relying on expensive externally collected training datasets.
Data Source
AI summary
A computer-implemented method is disclosed for segmenting a binarized document. The method includes extracting connected components from the binarized document and discriminating (for at least one of the connected components) whether it is a text component based on a homogeneity level value. The homogeneity level value is representative of the level of homogeneity within the local region of the connected component. The local region includes the connected component and at least one adjacent connected component. The homogeneity level value is based on at least one value representative of at least one image characteristic parameter determined for the connected component and on at least one value representative of the image characteristic parameter of the at least one adjacent connected component.