Layered Electronic Image Analysis for Handwriting Recognition
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Solution Overview
Problem
Traditional OCR methods are inaccurate and unreliable in recognizing and extracting handwritten notes or uncommon terms from patient health records and other documentation.
Innovation Solution
A layered processing and analysis approach using artificial intelligence and machine learning to identify and extract information from electronic images, recognizing both printed and handwritten content, and creating multi-layered images for enhanced searchability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCR methods are used to recognize textual elements, then processing simplicity is maintained, but recognition accuracy deteriorates especially for handwritten notes and uncommon terms
Solution Approach 1:
The patent segments the document processing into distinct layers: a first layer for printed text recognition using traditional OCR, and a second layer for handwritten text recognition using machine learning models. This segmentation allows each layer to specialize in specific text types, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces a hierarchical dimension to the processing architecture by implementing multiple recognition layers that operate at different levels of complexity. The first layer handles simple printed text, while the second layer addresses complex handwritten text, adding dimensional depth to the recognition process to accommodate varying text difficulties.
2Measurement precision
If manual review steps are included in document processing, then accuracy can be improved, but processing time and productivity deteriorate
Solution Approach 1:
The system performs self-service by automatically extracting and validating information through its multi-layered processing approach. The machine learning models autonomously recognize handwritten text and the system automatically resolves conflicts between layers without requiring manual intervention, thereby maintaining high accuracy while improving processing speed.
Solution Approach 2:
The patent implements feedback mechanisms where the system automatically validates extracted information by comparing results across different recognition layers and using confidence scores to determine when manual review is necessary. This feedback loop allows the system to maintain accuracy while minimizing unnecessary manual steps, improving overall productivity.
3Loss of information
If comprehensive text extraction is implemented, then information completeness is improved, but processing complexity and time increase
Solution Approach 1:
The patent applies preliminary action by pre-processing documents to identify and segment different text types before processing. The system预先 identifies which areas contain printed text and which contain handwritten text, allowing the appropriate recognition models to be applied in advance, thereby reducing overall processing time while maintaining comprehensive extraction.
Solution Approach 2:
The system implements partial action by selectively applying different processing depths to different document regions. Rather than uniformly processing every pixel, the system applies simple OCR to printed text regions and complex machine learning processing only to handwritten regions, reducing overall processing time while maintaining complete information extraction where needed.
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.