Document Image Analysis Using Anchor Field Recognition
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Solution Overview
Problem
Image processing techniques face difficulties in recognizing words, characters, and key fields of information in documents, often requiring excessive processing resources and human intervention.
Innovation Solution
The use of machine learning models to identify an anchor field in documents, which reduces the processing power needed to locate important, time-sensitive information by recognizing the document source and identifying additional fields closely tied to the anchor field.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing techniques are used to analyze documents, then the system can process documents, but it fails to recognize words, characters, and key fields of information accurately
Solution Approach 1:
The system performs preliminary actions by first identifying the document source type (e.g., receipt, invoice, bill) before extracting information. This preliminary classification enables the system to apply specialized recognition techniques tailored to each document type, significantly improving word, character, and field recognition accuracy compared to generic image processing approaches
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between the image processing system and the information extraction system. This intermediary classifies documents by source type and provides structured guidance for subsequent field identification, bridging the gap between raw image data and meaningful information extraction
2Productivity
If traditional image processing techniques are used to identify key fields, then the system can attempt to extract information, but it consumes excessive processing resources and time
Solution Approach 1:
The system extracts and focuses only on the most critical information fields based on the document source type classification. By identifying which fields are most important for each document type (e.g., date, amount, vendor for receipts), the system can allocate processing resources selectively rather than attempting to analyze every pixel and text element uniformly, significantly reducing computational overhead
Solution Approach 2:
The machine learning model performs preliminary classification of document sources before initiating full information extraction. This preliminary action filters and prioritizes which fields need detailed analysis, allowing the system to spend processing resources only on extracting information from the most relevant fields rather than exhaustively analyzing the entire document
3Extent of automation
If traditional image processing techniques are used, then the system can process documents, but human intervention is required to identify key fields
Solution Approach 1:
The system implements self-service automation by using machine learning models to automatically classify document sources and identify key fields without human intervention. The system serves itself by autonomously determining document types and extracting information, eliminating the need for manual field identification while maintaining high accuracy through learned patterns from training data
Data Source
AI summary
The present disclosure describes image analysis techniques that identify the source of a document. Once the source of the document is determined, the image analysis may locate one or more anchor fields in the document. The anchor fields may identify one or more additional fields that contain time-sensitive data and/or information. The image analysis performed herein may identify the time-sensitive data and/or information and process the data and/or information to schedule due dates and reminders.