Text Line Segmentation via Component Size Categorization
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Solution Overview
Problem
Existing text line segmentation methods in ICR/IWR systems face challenges with varying writing samples, particularly with slope, slant, and inter-line connections, leading to errors in text line segmentation and recognition.
Innovation Solution
A method that categorizes connected components into subsets based on size and applies Hough transform to detect candidate lines, with refinement using average line height and overlap analysis, and incorporates core region-based height estimation for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional Hough transform is applied to detect text lines, then text line detection can be performed, but segmentation accuracy deteriorates due to inter-line connections and varying writing styles
Solution Approach 1:
The patent segments connected components into different size categories (small, medium, large) and processes each category separately with appropriate strategies. Small components are treated as noise or punctuation, medium components as individual characters, and large components as potential text lines requiring Hough transform. This segmentation allows the system to handle varying writing styles and inter-line connections effectively while maintaining high segmentation accuracy.
Solution Approach 2:
The patent applies different processing strategies to different regions and components based on their local characteristics. For example, components above and below the detected text line are processed differently, and the Hough transform parameters are adjusted locally based on the distribution of connected components. This local quality approach enables the system to adapt to varying writing samples while maintaining robust text line detection.
2Reliability
If multi-dimensional Recurrent Neural Networks are used to by-pass line segmentation, then recognition accuracy is improved, but computational cost increases significantly
Solution Approach 1:
The patent segments the text line detection process into distinct stages: connected component analysis, candidate line generation using Hough transform, and refinement through geometric constraints. This segmentation allows the system to achieve high recognition accuracy through precise text line segmentation while avoiding the high computational cost of multi-dimensional RNNs by using efficient geometric algorithms.
Solution Approach 2:
The patent replaces the complex neural network-based approach with a geometric-mechanical approach using Hough transform and constraint-based refinement. This substitution maintains high recognition accuracy while significantly reducing computational cost, enabling real-time offline ICR/IWR system deployment.
3Reliability
If Hough transform is applied to all connected components, then comprehensive line detection is achieved, but computational complexity increases
Solution Approach 1:
The patent segments connected components by size and applies Hough transform only to the medium-sized components that are most likely to form text lines. Small components are filtered out as noise, and large components are processed separately. This segmentation dramatically reduces computational complexity while maintaining complete line detection through the coordinated processing of different component categories.
Solution Approach 2:
The patent applies Hough transform selectively to only the necessary subset of connected components (medium-sized ones) rather than all components. This partial action approach achieves comprehensive line detection by focusing computational resources on the most relevant components while avoiding unnecessary computations on small noise components and large multi-line components.
Data Source
AI summary
In a text line segmentation process, connected components (CCs) in document image are categorized into three subsets (normal, large, small) based on their sizes. The centroids of the normal size CCs are used to perform line detection using Hough transform. Among the detected candidate lines, those with line bounding box heights greater than a certain height are removed. For each normal size CC, if its bounding box does not overlap the bounting box of any line with an overlap area greater than a predefined fraction of the CC bounding box, a new line is added for this CC, which passes through the centroid of the CC and has an average slant angle. Each large size CCs are broken into two or more CCs. All CCs are then assigned to the nearest lines. A refinement method is also described, which can take any text line segmentation result and refine it.