Evidence Grounding for Text Image Information Extraction
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Solution Overview
Problem
Current computer vision systems face challenges in evidence grounding, specifically in locating complete text regions, classifying text contents according to predefined tags, and associating detected texts to form key-value pairs, particularly in varied formats of text images like federal tax forms.
Innovation Solution
The proposed solution involves a modified Conditional Random Fields machine learning system implemented as a Recurrent Neural Network and a modified Graph Attention Network, which detects text regions, recognizes text, classifies it according to predefined tags, and associates key-value pairs within the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computer vision systems are used for evidence grounding, then the system structure is simple, but the ability to locate complete text regions and classify text contents is insufficient
Solution Approach 1:
The system segments the evidence grounding task into multiple specialized modules: text region detection module, text content recognition module, and tag classification module. Each module focuses on a specific sub-task, improving overall precision while managing complexity through functional decomposition
Solution Approach 2:
The system introduces an intermediary processing layer that bridges traditional computer vision and advanced AI models. This intermediary layer includes feature extraction modules and data preprocessing components that prepare input data for sophisticated algorithms, enabling high precision without directly exposing system complexity
2Reliability
If traditional computer vision systems are used for evidence grounding, then the system is easy to operate, but the ability to associate detected texts with predefined tags is insufficient
Solution Approach 1:
The system implements self-service through automated tag association mechanisms. The tag classification module automatically matches detected text contents with predefined tags using learned patterns and relationships, eliminating manual intervention while ensuring high reliability in tag assignment
Solution Approach 2:
The system incorporates feedback loops where classification results are continuously refined based on confidence scores and validation rules. Low-confidence predictions trigger re-processing or alternative classification paths, ensuring high reliability while maintaining operational simplicity through automated decision-making
3Measurement precision
If advanced machine learning models are used to improve information extraction accuracy, then the extraction precision is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing input images to extract key features and detect text regions before applying sophisticated classification models. This preliminary processing reduces the complexity of subsequent operations and enables faster, more accurate information extraction
Solution Approach 2:
The system applies partial processing strategies by focusing computational resources on the most relevant text regions and tags. Instead of processing the entire image uniformly, the system selectively applies advanced models only to detected text regions, reducing overall processing time while maintaining extraction accuracy
Data Source
AI summary
Computer vision systems and methods for text classification are provided. The system detects a plurality of text regions in an image and generates a bounding box for each detected text region. The system utilizes a neural network to recognize text present within each bounding box and classifies the recognized text, based on at least one extracted feature of each bounding box and the recognized text present within each bounding box, according to a plurality of predefined tags. The system can associate a key with a value and return a key-value pair for each predefined tag.