Newspaper Article Polygon Generation for Data Extraction
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Solution Overview
Problem
Existing genealogical research systems face inaccuracies and computational inefficiencies when processing and extracting data from digitized historical documents, particularly newspaper images, due to variations in content arrangement, condition, and style across different time periods, leading to errors in identifying and segmenting articles and extracting actionable data.
Innovation Solution
The system employs specially designed models to generate polygons defining article boundaries within newspaper images, utilizing image correction techniques to detect columns, correct image skew, and remove outliers, and further processes article text using optical character recognition to predict article topics, entity names, and locations, improving accuracy and efficiency by adapting architectural models for newspaper image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional machine learning models are used to process newspaper images, then the system can identify articles, but the computational resource consumption is excessive
Solution Approach 1:
The system segments the newspaper image processing task into distinct stages: column detection, article boundary prediction, and polygon generation. By dividing the complex processing into smaller specialized sub-tasks, each stage can be optimized independently, reducing overall computational resource consumption while maintaining article identification accuracy.
Solution Approach 2:
The system performs preliminary column detection and image correction before article segmentation. By pre-processing the image to establish column structures and correct skew in advance, the subsequent article boundary prediction becomes more efficient and accurate, reducing the computational burden during the main processing stage.
2Measurement precision
If conventional image analysis techniques are used, then the system can process newspaper images, but the accuracy of article segmentation is insufficient due to variations in article size, location, style, and condition
Solution Approach 1:
The system applies different processing strategies to different regions of the newspaper image based on column detection results. Each column and article region receives localized analysis tailored to its specific characteristics, allowing the system to handle variations in article size, location, and style while maintaining high boundary detection accuracy.
Solution Approach 2:
The system transitions from traditional 2D image analysis to 3D polygon representation for article boundaries. By generating polygons with multiple vertices that can capture complex article shapes and layouts, the system achieves superior boundary detection accuracy across diverse newspaper formats and styles.
3Loss of information
If existing systems process digitized historical records, then they can extract information, but the models consume excessive computing resources
Solution Approach 1:
The system extracts only the essential features needed for article boundary detection and information extraction, rather than processing the entire image at full resolution. By selectively extracting relevant column structures and article regions, the system maintains complete information extraction capability while significantly reducing processing power consumption.
Data Source
AI summary
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating and providing actionable data from newspaper articles identified and segmented from digital newspaper images. For example, the disclosed systems segment articles of a newspaper image by using specially designed models to generate polygons defining article boundaries within the newspaper image. In some cases, the disclosed systems further determine article text from a polygon of an article for additional processing to determine an article topic, determine an article type, predict entity names within the article, and/or predict a locality associated with the article.