Automated Data Extraction from Scanned Documents Using OCR and Layout Analysis
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Solution Overview
Problem
The manual extraction of data from electronic documents, such as scanned images, is time-consuming and prone to errors, leading to inefficiencies and backlogs due to the lack of a text layer and the need for manual page identification and optical character recognition (OCR) correction.
Innovation Solution
A structured data processing system with a data extraction solver that performs image preprocessing, optical character recognition, page classification, layout analysis, and table detection to automatically extract alphanumeric data from scanned documents, using techniques like rotation correction, morphological transformations, and support vector machine classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual extraction of data from electronic documents is performed, then data can be extracted from scanned images, but the process is time-consuming and costly
Solution Approach 1:
The patent replaces the manual mechanical process of data extraction with an automated computer vision system that uses image processing algorithms, OCR technology, and machine learning models to automatically extract, recognize, and validate data from scanned documents, eliminating the need for human operators to manually transcribe information
Solution Approach 2:
The system enables documents to be processed automatically without human intervention by implementing self-correcting OCR capabilities, automated validation rules that flag anomalies, and intelligent routing that directs complex documents to appropriate handlers, allowing the extraction system to service itself for routine operations
2Measurement precision
If manual page identification and OCR correction are performed, then accurate text recognition can be achieved, but the process is fraught with error and time-consuming
Solution Approach 1:
The patent implements feedback mechanisms where extracted data is automatically validated against predefined business rules, data type constraints, and cross-field relationships. Invalid or anomalous data triggers automated correction attempts or flags for review, creating a closed-loop system that continuously improves accuracy through validation feedback
Solution Approach 2:
The system performs preliminary image preprocessing operations including deskewing, de-noising, and contrast enhancement before OCR is applied. Pages are pre-classified by document type and layout pattern, and preprocessing parameters are adjusted in advance based on the detected document characteristics, ensuring optimal OCR conditions before text recognition begins
3Productivity
If automated data extraction is implemented, then efficiency is improved, but the system complexity increases
Solution Approach 1:
The patent divides the complex data extraction system into modular functional components: image preprocessing module, document classification module, OCR engine, table detection module, data validation module, and exception handling module. Each module performs a specific function and can be independently configured, maintained, and replaced, reducing overall system complexity through functional segmentation
Data Source
AI summary
A structured data processing system includes hardware processors and a memory in communication with the hardware processors. The memory stores a data structure and an execution environment. The data structure includes an electronic document. The execution environment includes a data extraction solver configured to perform operations including identifying a particular page of the electronic document; performing an optical character recognition (OCR) on the page to determine a plurality of alphanumeric text strings on the page; determining a type of the page; determining a layout of the page; determining at least one table on the page based at least in part on the determined type of the page and the determined layout of the page; and extracting a plurality of data from the determined table on the page. The execution environment also includes a user interface module that generates a user interface that renders graphical representations of the extracted data; and a transmission module that transmits data that represents the graphical representations.