Entity Data Identification Using NLP and OCR for Complex Documents
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computer vision and process automation technologies are unable to effectively review and extract data from complex, data-rich electronic documents, such as those containing a mixture of text, tables, and images, leading to the need for manual review which is time-consuming and prone to human error.
Innovation Solution
A computer-implemented method using a trained natural language processing model and optical character recognition processor to extract text data, determine candidate entity data, access n-gram words from a knowledge base, and calculate similarity scores to identify and select optimum entity data, enabling the review of complex data sets with high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing computer vision and process automation technology is used to review electronic documents, then simple documents can be processed automatically, but complex data-rich documents cannot be reviewed accurately
Solution Approach 1:
The document processing system segments complex data-rich documents into distinct components (text regions, tables, images, formulas) and applies specialized processing models to each segment type. This segmentation enables the system to handle diverse document formats accurately while maintaining adaptability across different document complexities.
Solution Approach 2:
The system employs a composite architecture combining multiple AI models (OCR for text extraction, table recognition models for tabular data, image processing for visual elements) to process different document components. This composite approach allows the system to maintain high reliability across varied document types by leveraging the strengths of each specialized model.
2Reliability
If manual review of complex documents is performed, then accurate data extraction is possible, but the process is time-consuming and expensive
Solution Approach 1:
The system replaces manual mechanical review processes with automated AI-based processing. Machine learning models automatically extract and validate data from complex documents, achieving both high accuracy and rapid processing speeds, thereby eliminating the trade-off between manual accuracy and automated speed.
Solution Approach 2:
The system incorporates self-correction and validation mechanisms where the AI model automatically identifies and corrects extraction errors, validates data consistency, and adjusts processing parameters without human intervention. This self-service capability maintains high accuracy while enabling rapid automated processing of complex documents.
3Productivity
If existing automation technology is used, then processing speed is fast, but it cannot handle mixed content types (text, tables, images)
Solution Approach 1:
The system implements a universal processing framework that can handle multiple content types (text, tables, images, formulas) within a single automated workflow. The multi-functional architecture processes diverse document elements simultaneously, maintaining high processing speed while achieving versatility across mixed content types.
Data Source
AI summary
A data processing system receives a plurality of electronic documents in image format, and extracts text data using an optical character recognition processor. The system determines a plurality of candidate entity data and candidate context data based on the extracted text data using a trained natural language processing closed-domain question answering model. The system accesses n-gram words stored in a knowledge base, and determines similarity scores between each candidate context data and each of the n-gram words. The system determines a weighted average of the similarity scores, and selects an optimum entity data from the plurality of candidate entity data based on the weighted average of the similarity scores.