ML Model for Document Content Extraction
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Solution Overview
Problem
Conventional software solutions struggle to accurately extract content from documents, especially when documents are in non-standard formats or contain distorted or blurred images, requiring high computational capacity and processing time for individual analysis.
Innovation Solution
The system uses a machine learning model to automatically classify document text, generate labels, and predict content for forms, allowing for the automatic completion of form fields and providing confidence scores for predicted content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional software solutions are used to extract content from documents, then the extraction process can be automated, but the accuracy is insufficient especially for non-standard formats and distorted images
Solution Approach 1:
The patent transforms the document image into multiple parameter representations including histogram of oriented gradients (HOG), scale-invariant feature transform (SIFT), and other feature vectors. These parameter transformations enable the system to capture essential characteristics of documents in various formats and conditions, improving both accuracy and adaptability simultaneously
Solution Approach 2:
The patent replaces conventional rule-based and template-matching software solutions with a machine learning-based recognition system. This substitution allows the system to learn from training data and adapt to diverse document formats and image qualities, overcoming the limitations of rigid conventional methods
2Measurement precision
If individual document analysis is performed to handle distorted or blurred images, then extraction accuracy may improve, but computational capacity and processing time requirements increase significantly
Solution Approach 1:
The patent performs feature extraction and parameter transformation (HOG, SIFT, etc.) on the document image before the actual recognition process. This preliminary processing converts the image into compact feature representations that capture essential information while reducing computational complexity for subsequent matching operations
Solution Approach 2:
The patent creates feature representations (copies of essential characteristics) of the document image through histogram of oriented gradients and scale-invariant feature transform. These feature copies enable efficient comparison and matching without requiring intensive processing of the original high-resolution image, thus improving processing speed while maintaining accuracy
Data Source
AI summary
A server may automatically determine a classification for document text of an electronic document and display a graphical indication of the classification of document text in a first graphical region of a first graphical user interface. In response to the server receiving an approval of the classification, the server may generate a label for the document text based on the classification and train a machine learning (ML) model using the label and the electronic document. Furthermore, the server may execute the trained ML model for a second electronic document. For at least one field of a form on a webpage, the server may automatically complete a widget embedded in the web page, using the trained ML model display a second graphical indication of text in the second electronic document providing data for the at least one field.