Information Extraction System Layout Adaptability
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Solution Overview
Problem
Conventional methods for recognizing and extracting structured information from documents or images face challenges such as high data labeling costs, poor generalization, and reliance on fixed templates, leading to inefficiencies in processing diverse layouts and formats.
Innovation Solution
A method that recognizes multiple text items in an image, classifies them into name and content text items based on semantics, and performs matching operations to determine matched pairs, allowing for flexible layout handling and reduced data labeling requirements through incremental learning and modular training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional template-based methods are used for information extraction, then the system can process fixed-format documents, but it cannot handle diverse layouts and formats effectively
Solution Approach 1:
The patent segments the information extraction process into distinct modules: text recognition module, layout analysis module, and information extraction module. Each module handles specific aspects independently, allowing the system to adapt to various layouts while maintaining extraction accuracy through specialized processing for each segment.
Solution Approach 2:
The system dynamically adjusts its processing approach based on the detected layout structure. Rather than using fixed templates, the layout analysis module identifies the actual document structure and dynamically configures the extraction process to match the observed format, enabling handling of diverse layouts while maintaining reliability.
2Reliability
If extensive data labeling is performed to improve extraction accuracy, then the system achieves better performance, but the data labeling cost increases significantly
Solution Approach 1:
The patent performs preliminary layout analysis and text recognition before the actual information extraction. By pre-processing the document to identify structures, text regions, and potential information fields, the system reduces the complexity of subsequent extraction tasks, thereby reducing the amount of labeled training data needed while maintaining high extraction accuracy.
Solution Approach 2:
The layout analysis module serves as an intermediary between text recognition and information extraction. It bridges these stages by structuring the recognized text according to document layout, providing organized input to the extraction module and reducing the labeling burden through intermediate structure inference.
3Productivity
If fixed templates are used for information extraction, then the processing speed is maintained, but the system cannot generalize to different document formats
Solution Approach 1:
The patent implements a universal layout analysis framework that can handle multiple document formats and layouts through a single system. The analysis module identifies common structural patterns across different document types and applies generalized extraction rules, enabling the system to maintain processing speed while achieving broad format generalization capability.
Data Source
AI summary
The disclosure provides a method and a device for processing information, an electronic device, and a storage medium, belonging to a field of artificial intelligence including computer vision, deep learning, and natural language processing. In the method, the computing device recognizes multiple text items in the image. The computing device classifies multiple text items into a first set of name text items and a second set of content text items based on semantics of the text items. The computing device performs a matching operation between the first set and the second set based on a layout of the text items in the image, and determines matched name-content text items. The matched name-content text items include a name text item in the first set and a content text item matching the name text item and in the second set. The computing device outputs the matched name-content text items.