Contextualizing Indeterminable Document Data via Determinable Metadata
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Solution Overview
Problem
Current systems face challenges in efficiently extracting and classifying information from physical documents, particularly in industries like banking and healthcare, where handwritten information is indeterminable by machines, leading to manual processing bottlenecks and inefficiencies.
Innovation Solution
A system that contextualizes machine indeterminable information by extracting machine determinable information from electronic document images using OCR and IWR, presenting contextualized data to users for identification and further processing, thereby facilitating efficient handling and routing of documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processing is used for handwritten information, then accuracy can be maintained through human judgment, but productivity decreases due to manual processing bottlenecks
Solution Approach 1:
The processing system is segmented into two distinct components: a machine processing component that handles machine-determinable information (extracting text, structure, and metadata from documents), and a human review component that handles machine-indeterminable information (handwritten fields, ambiguous data). This segmentation allows the machine to handle high-volume routine processing while humans focus only on complex cases, thereby improving both productivity and accuracy.
Solution Approach 2:
The system introduces an intermediary contextualization layer that bridges machine-determinable and machine-indeterminable information. By using machine-determinable data (document type, extracted text, metadata) to contextualize and pre-process handwritten information, the system prepares data in a way that reduces the cognitive load on human reviewers and improves their efficiency while maintaining accuracy.
2Measurement precision
If complete manual review is performed on all document information, then accuracy is maintained, but loss of time increases due to processing bottlenecks
Solution Approach 1:
The system performs preliminary processing of documents using machine learning and OCR technologies to extract machine-determinable information before human review. This preliminary action includes document classification, text extraction, and contextualization of handwritten fields, which prepares the data in advance and reduces the time required for human reviewers to process each document while maintaining accuracy.
3Productivity
If machine processing is used for all information, then productivity increases, but measurement precision decreases for handwritten information
Solution Approach 1:
The system applies different processing qualities to different parts of the document: machine processing with high automation for machine-determinable information (printed text, structured data, metadata) and human processing with contextual support for machine-indeterminable information (handwritten fields, complex patterns). This local differentiation of processing quality optimizes both productivity and accuracy for each type of information.
4Reliability
If manual classification and routing are performed, then reliability of document handling is maintained, but productivity decreases
Solution Approach 1:
The classification and routing functions are segmented between machine automation and human judgment. The machine handles initial document classification based on machine-determinable information (document type, extracted text, metadata), and humans handle final verification and complex routing decisions. This segmentation maintains reliability through human oversight while improving productivity through machine pre-processing.
Solution Approach 2:
The system implements feedback loops where machine processing results are reviewed and corrected by humans, and these corrections are fed back to improve the machine learning models. This continuous feedback mechanism ensures reliability is maintained and improved over time while the system handles increasing volumes of documents efficiently.
Data Source
AI summary
A system for contextualizing machine indeterminable information based on machine determinable information may include a memory, an interface, and a processor. The memory may store an electronic document image which may include information determinable by a machine and information indeterminable by a machine. The processor may be operative to receive, via the interface, the electronic document image. The processor may determine the machine determinable information of the electronic document image and may identify the machine indeterminable information of the electronic document image. The processor may contextualize the machine indeterminable information based on the machine determinable information. The processor may present the contextualized machine indeterminable information to the user to facilitate interpretation thereof. In response thereto, the processor may receive, via the interface, data representative of a user determination associated with the machine indeterminable information.