Document Pattern Recognition for Remittance Data Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Financial and accounting departments face challenges in manually reviewing and entering remittance information from various document types, leading to time-consuming processes and potential errors.
Innovation Solution
Implementing a system that uses machine learning algorithms to analyze documents, correlate them with historical patterns, and extract remittance information in real-time, reducing the need for manual entry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review and entry of remittance information is performed, then data accuracy can be maintained through human judgment, but processing time and labor costs increase significantly
Solution Approach 1:
The patent replaces the mechanical human review process with an optical character recognition (OCR) system and machine learning algorithms. The system captures document images, converts them to text through OCR, and automatically extracts remittance information using pattern recognition, eliminating manual mechanical entry while maintaining accuracy through automated validation rules.
Solution Approach 2:
The system enables self-service by allowing the document processing system to automatically extract, validate, and enter remittance information without human intervention. The machine learning model continuously learns from historical data to improve its own accuracy, and the system automatically handles routing and data entry, making the process self-sufficient.
2Adaptability or versatility
If multiple document types and templates are processed manually, then comprehensive data extraction is possible, but the complexity and time required for each document increases
Solution Approach 1:
The patent implements a universal processing system that handles multiple document types (invoices, receipts, remittance advices, etc.) and various templates through a single machine learning platform. The system uses pattern recognition to identify document types automatically and applies appropriate extraction rules, making one system capable of processing diverse financial documents without requiring separate manual procedures for each type.
Solution Approach 2:
The system dynamically adjusts processing parameters based on document characteristics. The machine learning model analyzes document features such as layout, format, and content patterns to automatically modify extraction parameters and validation rules, adapting to different document types and templates without requiring manual reconfiguration of the processing system.
3Ease of operation
If manual entry of remittance information is performed, then flexibility in handling exceptional cases is maintained, but productivity and throughput are significantly reduced
Solution Approach 1:
The system incorporates feedback mechanisms where extracted data is validated against business rules and historical patterns. When exceptions or low-confidence extractions are detected, the system automatically flags them for review or uses machine learning to learn from correction patterns, maintaining flexibility while processing the majority of documents automatically at high speed.
Solution Approach 2:
The system performs preliminary validation and classification of documents before full processing. By pre-identifying document types, confidence levels, and potential exceptions, the system can route documents appropriately in advance, maintaining flexibility for exceptional cases while ensuring high-speed automated processing for standard documents.
Data Source
AI summary
Novel tools and techniques are provided for implementing automatic document pattern recognition and analysis. In various embodiments, a computing system might receive one or more first documents containing first remittance information. The computing system might analyze the one or more first documents to extract first remittance information from the first document by obtaining one or more historical documents and correlating the one or more first documents to the one or more historical documents. Next, based on the correlation of the one or more first documents to the one or more historical documents, the computing system might determine at least one historical document that correlates to the one or more first documents. Additionally, the computing system might extract the first remittance information from the one or more first documents based on the at least one historical document.