Document Data Extraction via ML Template Generation
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Solution Overview
Problem
Existing OCR-based solutions for extracting data from documents are slow, expensive, and require manual template creation, making them inefficient and labor-intensive.
Innovation Solution
A method using machine learning to automate the template creation process by analyzing similarities between sample documents and their corresponding data sets, allowing for the generation of templates that can extract desired data from similar documents without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If OCR-based solutions are used for data extraction, then data can be extracted from documents, but the process becomes slow and expensive
Solution Approach 1:
The patent segments the document processing task into distinct components: template creation phase and data extraction phase. The template is created once from a sample document and then reused for extracting data from multiple similar documents, avoiding the need to process each document individually through OCR, thereby improving speed and reducing cost.
Solution Approach 2:
The patent performs preliminary action by creating a template from a sample document before actual data extraction. This template captures the structure and data locations, which are then reused for extracting data from multiple documents, eliminating the need to perform OCR and template creation for each document, thus improving productivity and reducing cost.
2Measurement precision
If manual template creation is performed for each document, then accurate data extraction can be achieved, but the process becomes time-consuming
Solution Approach 1:
The patent creates a template by copying the structure and data location information from a single sample document. This template is then reused for extracting data from multiple similar documents, maintaining accuracy while eliminating the need to manually create templates for each document, thus resolving the contradiction between accuracy and time consumption.
Solution Approach 2:
The patent creates a universal template that can be applied to multiple documents with similar structures. This single template serves the function of guiding data extraction from numerous documents, eliminating the need for repeated manual template creation while maintaining extraction accuracy across all documents.
3Adaptability or versatility
If templates are manually created for every document type, then data extraction can be customized, but the complexity of the system increases
Solution Approach 1:
The patent enables the system to automatically adapt to different document types by using a sample document to generate the appropriate template. The system serves itself by learning the structure from the sample and applying it automatically, eliminating the need for complex manual configuration while maintaining adaptability to different document formats.
Solution Approach 2:
The patent changes the approach from manually configuring complex templates to automatically generating templates by analyzing sample documents. The system adjusts its behavior based on the parameters extracted from the sample document structure, maintaining versatility while reducing system complexity through automation.
Data Source
AI summary
Methods, systems and computer-readable media for extracting data from a document. One method includes receiving a document in a text format and assigning a first signature to the document. The method also includes matching the first signature to a second signature of a template and extracting data a user desires to have extracted from the document, wherein instructions for locating the data are stored within the template.