Layered Electronic Document Analysis for Handwritten OCR Accuracy
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Solution Overview
Problem
Traditional OCR methods are inaccurate and unreliable in recognizing and extracting handwritten notes or uncommon terms from electronic documents, particularly in patient health records and other documentation.
Innovation Solution
A layered processing and analysis approach using artificial intelligence and machine learning technologies to identify and extract information from electronic images, including a first layer of context extraction, a second layer of imaging content extraction, and a third layer of JSON data parsing, followed by creating a unified searchable document.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCR methods are used to recognize textual elements, then the processing speed is maintained at acceptable levels, but the accuracy and reliability of recognizing handwritten notes and uncommon terms deteriorates significantly
Solution Approach 1:
The patent segments the document analysis process into multiple distinct layers: visual processing layer for image analysis, textual processing layer for OCR, structured data processing layer for JSON parsing, and context integration layer for combining results. This segmentation allows each layer to specialize in specific tasks, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces a layered processing architecture that adds dimensional complexity to the traditional single-stage OCR approach. By processing documents through multiple concurrent layers (visual, textual, structured data, context), the system achieves higher recognition accuracy without sequential processing bottlenecks, effectively using dimensional expansion to resolve the accuracy-speed tradeoff.
2Loss of information
If traditional single-layer OCR processing is used, then the processing time is shorter, but the completeness of extracted information deteriorates
Solution Approach 1:
The visual processing layer performs preliminary analysis of the document image before textual extraction, identifying handwritten notes, uncommon terms, and their locations. This preliminary action guides subsequent OCR processing to focus on relevant areas, ensuring complete information extraction while reducing overall processing time by avoiding unnecessary analysis of entire documents uniformly.
Solution Approach 2:
The patent implements continuous processing where multiple layers operate simultaneously and feed results to each other. The visual, textual, structured data, and context layers process information in parallel with continuous feedback loops, maintaining information extraction completeness while eliminating sequential processing delays through sustained concurrent operation.
3Reliability
If manual review steps are included in the process, then the accuracy of document analysis is improved, but the productivity and efficiency deteriorate
Solution Approach 1:
The context integration layer incorporates feedback mechanisms that automatically review and validate extracted information by comparing results across multiple layers and against established context rules. This automated feedback loop detects and corrects errors without requiring manual intervention, maintaining high reliability while preserving processing throughput by eliminating manual review bottlenecks.
Solution Approach 2:
The system performs self-validation through its multi-layer architecture where each layer automatically checks and verifies its output against other layers and predefined context rules. The structured data processing layer and context integration layer autonomously identify and correct extraction errors, enabling the system to serve its own quality control needs without external manual review, thus maintaining both reliability and productivity.
Data Source
AI summary
A machine translation of a document is created via a compilation of services by mapping textual content from an image to create a plurality of mapped locations correspondent to at least one object from the image, populating each of the mapped locations with at least one character indicative of the object, each character sharing at least one similar attribute, adding to the image the populated mapped locations, and highlighting at least a portion of the textual content in accordance with the populated at least one character. A compilation of services is provided for identifying, extracting, and assessing electronic images by using a layered approach that reduces time and improves reviewing of medical records and other kinds of documentation.