Neural Network Image Segmentation for Newspaper Articles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing object detection and segmentation methods are poorly adapted to identify and segment individual articles within scanned newspaper images, due to variations in layout, size, shape, and configuration of articles, as well as the dense and text-heavy nature of newspaper content.
Innovation Solution
A system and method that incorporates a custom resizing algorithm using a convolutional neural network to learn features important for article segmentation, combined with a novel region-proposing algorithm that synergistically combines different image-segmentation paradigms, such as Mask-RCNN and YOLO, to improve segmentation output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing object detection methods (e.g., Faster-RCNN) are used to identify articles in newspaper images, then precision in identifying focal objects is improved, but recall deteriorates because the model prioritizes top k regions and misses less prominent articles
Solution Approach 1:
The patent applies dynamics by making the region proposal process adaptive and iterative. Instead of a static top-k selection, the system dynamically adjusts region proposals across multiple passes, refining selections based on previous results to capture both prominent and less prominent articles effectively
Solution Approach 2:
The patent uses partial action by processing regions in multiple passes rather than attempting to identify all articles in a single comprehensive pass. This allows the system to focus computational resources on different subsets of articles across iterations, improving overall recall without sacrificing precision
2Stability of the object's composition
If rigid user-generated templates are used for segmenting newspaper articles, then consistency in segmentation is improved, but adaptability deteriorates because no two newspaper pages have the same layout
Solution Approach 1:
The patent replaces static templates with dynamic, data-driven region proposals that adapt to each newspaper page's unique layout. The system learns article characteristics from training data and automatically adjusts to varying layouts, maintaining consistency through learned patterns while achieving adaptability to diverse configurations
Solution Approach 2:
The patent changes the fundamental parameter of segmentation from fixed template coordinates to learned feature-based region proposals. This allows the system to maintain consistency through learned article characteristics while adapting to different layouts by adjusting region parameters based on actual content rather than predetermined templates
3Productivity
If conventional downsampling algorithms are used to reduce image size, then processing speed is improved, but text-specific features are lost making articles unrecognizable
Solution Approach 1:
The patent changes the downsampling approach from conventional uniform reduction to selective feature-preserving reduction. The system adjusts downsampling parameters dynamically based on region importance and text density, maintaining critical text features while still achieving computational efficiency through intelligent parameter modulation
4Reliability
If existing segmentation models are trained on large datasets, then generalization capability is improved, but training time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing images to extract and emphasize article-specific features before main training. This preliminary feature extraction allows the model to achieve better generalization with fewer training iterations, reducing overall training time while maintaining or improving generalization capability
Solution Approach 2:
The patent extracts and focuses on critical article features separately from the full image data. By taking out and emphasizing key textual and structural features, the system achieves effective training with reduced data requirements and fewer iterations, balancing generalization capability with training efficiency
Data Source
AI summary
Systems and methods for identifying and segmenting objects from images include a preprocessing module configured to adjust a size of a source image; a region-proposal module configured to propose one or more regions of interest in the size-adjusted source image; and a prediction module configured to predict a classification, bounding box coordinates, and mask. Such systems and methods may utilize end-to-end training of the modules using adversarial loss, facilitating the use of a small training set, and can be configured to process historical documents, such as large images comprising text. The preprocessing module within the systems and methods can utilize a conventional image scaler in tandem with a custom image scaler to provide a resized image suitable for GPU processing, and the region-proposal module can utilize a region-proposal network from a single-stage detection model in tandem with a two-stage detection model paradigm to capture substantially all particles in an image.