Anomaly Detection in Text Using Variational Autoencoders
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Solution Overview
Problem
Existing systems fail to accurately and automatically detect anomalous text in regulatory contexts, such as bolded allergens on food labels, due to variations in font appearance and overfitting issues in machine learning models.
Innovation Solution
A machine learning-based approach using Deep Neural Networks and multi-task learning with variational autoencoders (VAEs) for anomaly detection in text, focusing on visual appearance rather than semantic meaning, with techniques like optical character recognition (OCR) and clustering strategies to identify bolded or italicized words.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning models are used for anomaly detection, then the system can identify anomalous text, but the models suffer from overfitting and false positives due to variations in font appearance
Solution Approach 1:
The patent segments the text detection task into multiple specialized sub-tasks: detecting bold text, italic text, monospace text, and subscript text separately. Each sub-task is handled by a dedicated detection model trained on specific font style characteristics. This segmentation prevents overfitting by focusing each model on a narrow, well-defined pattern rather than attempting to generalize across all font variations.
Solution Approach 2:
The patent transforms the anomaly detection problem from a classification task into a parameter-based detection task. Instead of training models to classify anomalous vs. normal text, the system uses OCR to extract text and its associated parameters (font weight, style, spacing). Anomalies are detected by comparing these extracted parameters against expected parameter ranges for regulatory contexts, eliminating overfitting issues inherent in traditional ML classification approaches.
2Adaptability or versatility
If the system adapts to varying font styles and sizes, then it can detect anomalous text in different formats, but the complexity of the detection system increases
Solution Approach 1:
The patent introduces OCR (Optical Character Recognition) as an intermediary component between the input image and the anomaly detection logic. The OCR system extracts text along with its associated parameters (font weight, style, size, spacing) from the input image. These extracted parameters serve as a standardized intermediate representation that simplifies subsequent anomaly detection, eliminating the need for complex image processing pipelines for each font style.
Solution Approach 2:
The patent creates a universal detection framework that handles multiple font styles (bold, italic, monospace, subscript) and sizes through a single parameter-comparison mechanism. The system uses one anomaly detection module that works universally across all text types by comparing extracted parameters against regulatory expectations, rather than requiring separate specialized modules for each font style or size category.
3Measurement precision
If the system focuses on visual appearance rather than semantic meaning, then it can accurately detect formatted text, but the complexity of feature extraction increases
Solution Approach 1:
The patent uses OCR as an intermediary that automatically performs the complex feature extraction from visual text appearance. The OCR system handles the difficult tasks of analyzing font weight, style, size, and spacing by converting visual characteristics into structured parameter data. This intermediary approach simplifies the overall system architecture by replacing complex custom feature extraction pipelines with a proven, standardized OCR technology that already possesses sophisticated visual analysis capabilities.
Data Source
AI summary
Systems and techniques are generally described for anomaly detection in text. In some examples, text data comprising a plurality of words may be received. An image of a first word of the plurality of words may be generated. A feature representation of the first word may be generated using a variational autoencoder. A score may be generated based at least in part on the feature representation. In various examples, the score may indicate a likelihood that an appearance of the first word in the image of the first word is anomalous with respect to at least some other words of the plurality of words.