Machine Learning Form Recognition for Unstructured Data
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Solution Overview
Problem
Existing methods for transforming unstructured data representing standardized forms, such as invoices, into structured data are inefficient and laborious, often requiring manual entry and the creation of supplier-specific databases that need regular updating.
Innovation Solution
A method that uses machine learning techniques to identify and categorize visual patterns in unstructured data, allowing for the automatic transformation of unstructured data into structured data without relying on specific databases for each supplier.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual entry methods are used to transform unstructured form data to structured data, then data accuracy can be maintained through human verification, but processing time and labor complexity increase significantly
Solution Approach 1:
The patent segments the form processing task into multiple stages: optical character recognition (OCR) to extract text, machine learning classification to identify field types, and structured data assembly. This segmentation allows automated processing while maintaining accuracy through specialized handling at each stage.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a bridge between unstructured form images and structured data. This intermediary classifies and categorizes extracted text into appropriate fields, enabling automated transformation without direct manual intervention while preserving data accuracy.
2Productivity
If supplier-specific databases are created to store form data, then data retrieval and processing efficiency improve, but system complexity and maintenance burden increase
Solution Approach 1:
The patent creates a universal machine learning model that can process multiple types of standardized forms (invoices, purchase orders, delivery notes) without requiring separate databases for each supplier. This universal approach maintains productivity by handling diverse forms through a single system while reducing complexity compared to maintaining multiple supplier-specific databases.
Solution Approach 2:
Instead of creating and maintaining separate physical databases for each supplier, the patent uses a copied approach where the same machine learning model and processing pipeline are applied to different form types. This eliminates the need for complex database management while maintaining efficient data retrieval through standardized processing.
3Productivity
If automated recognition systems are implemented to reduce manual entry, then processing speed increases, but adaptability to different form formats decreases
Solution Approach 1:
The patent implements a dynamic machine learning model that can adapt to different form formats and layouts. The model learns from training data encompassing various standardized form types and can dynamically adjust its classification and extraction processes to handle different formats, maintaining both processing speed and adaptability.
Solution Approach 2:
The patent changes the parameters of the recognition system by using machine learning models with adjustable weights and thresholds that can be optimized for different form types. This allows the system to maintain high processing speed while adapting to various standardized form formats through parameter optimization rather than rigid programming.
Data Source
AI summary
The present invention concerns a method for transforming an unstructured set of data representing a standardized form to a structured set of data. The method comprises a processing phase comprising the steps of determining a plurality of data blocks in the unstructured set of data using learning parameters determined using a plurality of samples, each data block corresponding to a visual pattern on the standardized form and being categorized to a known class, of processing data in each data block and of forming a structured set of data using the processed data from each data block, according to the class of this block.