Document Region Identification for Spatial Data Extraction
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Solution Overview
Problem
Current data extraction systems from scanned documents, such as invoices, are inefficient due to the need for manual labor and are prone to errors, as they rely on Optical Character Recognition (OCR) which loses spatial information and requires predefined rules or templates that fail when dealing with varying document layouts and formats.
Innovation Solution
A system that uses machine learning and deep learning with neural networks to identify regions of interest in documents, applying OCR only to specific regions to preserve spatial information and extract key data, and then cleans the data using post-processing techniques to present it in a usable format like JSON.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If OCR technology is used to automatically translate text data from scanned documents, then data extraction speed is improved, but spatial information is lost and accuracy deteriorates
Solution Approach 1:
The patent segments the document processing into two distinct phases: first, structure analysis is performed on the entire document to identify regions of interest and their spatial relationships; second, OCR is applied only to these identified regions. This segmentation allows the system to preserve spatial information at the document level while still achieving automated data extraction, resolving the contradiction between speed and spatial information preservation.
2Measurement precision
If pre-defined rules or templates are used for data extraction, then extraction accuracy is improved for standard formats, but adaptability to different document layouts deteriorates
Solution Approach 1:
The patent implements a dynamic rule generation system that creates extraction rules based on the actual structure of each document. Instead of using fixed pre-defined templates, the system analyzes the document's layout, identifies regions of interest, and generates appropriate extraction rules adaptively. This allows the system to maintain high accuracy for each specific document type while being versatile across different formats and vendors.
3Measurement precision
If manual labor is employed to key in data from physical documents, then data extraction accuracy is improved, but processing time increases
Solution Approach 1:
The patent introduces an intermediary structure analysis system that acts as a bridge between manual data entry and automated OCR processing. This intermediary analyzes document structures, identifies regions of interest, and guides the OCR process, thereby reducing the need for manual intervention while maintaining high accuracy. The system combines the accuracy benefits of manual understanding with the speed of automation.
4Ease of operation
If text search is performed on extracted data to determine relevant information, then data retrieval is simplified, but error rate increases due to OCR formatting limitations
Solution Approach 1:
The patent performs preliminary structure analysis and region identification before OCR and text extraction. By pre-identifying regions of interest and their spatial relationships, the system ensures that the subsequent text extraction and search operations are performed on correctly formatted and contextualized data. This preliminary action prevents formatting errors from propagating through the extraction process, maintaining both simplicity and reliability.
Data Source
AI summary
A system and method to extract data from regions of interest on a document is disclosed. A storage device stores an image derived from a document having text information. A region of interest identification engine scans the image and identifies at least one region of interest on the image. An optical character recognition module is operable to convert image data in the region of interest to machine readable data. A classification engine classifies the data obtained by the optical character recognition module from the region of interest.