Neural Network Text Extraction Using Anchor Words
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Solution Overview
Problem
Traditional automated processes struggle to accurately and efficiently extract information from visual images, such as those containing blurring, reflections, or noise, due to their limitations in handling low-quality images and specific templates, leading to manual review and potential errors.
Innovation Solution
A system comprising a processor that analyzes visual images to determine anchor words and text strings using neural networks, applies bounding boxes for text detection, and employs machine learning and rules-based techniques for accurate text recognition, even in skewed images, to extract relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional automated processes are used to extract information from visual images, then processing can be automated, but accuracy and efficiency deteriorate due to limitations in handling low-quality images and specific templates
Solution Approach 1:
The patent replaces traditional automated optical character recognition (OCR) systems with a neural network-based system. The neural network analyzes images and generates anchor words and text strings without relying on predefined templates or mechanical processing rules, enabling accurate extraction from low-quality images including those with blurring, reflections, or noise.
Solution Approach 2:
The system changes the fundamental parameters of image analysis by using neural networks to detect text information differently from traditional methods. Instead of relying on fixed templates or simple image processing, the neural network learns patterns from training data and adapts to various image qualities, transforming how text extraction parameters are handled.
2Productivity
If traditional automated processes are used for text extraction, then processing speed can be maintained, but reliability deteriorates due to manual review requirements and potential errors
Solution Approach 1:
The patent replaces traditional template-based automated extraction with neural network-based extraction that maintains processing speed while improving reliability. The neural network's ability to learn from training data and handle variations in image quality eliminates the need for manual review, reducing errors while maintaining automated processing capability.
3Measurement precision
If neural network techniques are used to analyze visual images, then accuracy of text extraction improves, but device complexity increases
Solution Approach 1:
The patent segments the image analysis process into distinct components: neural network-based anchor word detection, text string generation, and bounding box localization. This segmentation allows the complex neural network processing to be broken down into manageable stages, improving accuracy while making the system more controllable and maintainable.
Data Source
AI summary
Scene text information extraction of desired text information from an image can be performed and managed. An information management component (IMC) can determine an anchor word based on analysis of an image. To facilitate determining desired text information in the image, IMC can re-orient the image to zero or substantially zero degrees if it determines that the orientation is skewed. IMC can utilize a neural network to determine and apply bounding boxes to text strings in the image. Using a rules-based approach or machine learning techniques, employing a trained machine learning component, IMC can utilize the anchor word along with inline grouping of textual information in the image, deep text recognition analysis, or bounding box prediction to determine or predict the desired text information in the image. IMC can facilitate presenting the desired text information, anchor word, or other information obtained from the image in an editable format.