Dynamic Zone Mapping for Document Text Extraction
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Solution Overview
Problem
Current document capture and extraction solutions require manual and error-prone processes for zoning and mapping text zones to fields, especially when dealing with documents that require precise identification and positioning, which can be challenging due to image clarity issues and user interface limitations.
Innovation Solution
A method and system that dynamically map zones by using a search and analytics engine to provide type-ahead suggestions based on indexed text data, allowing users to select and associate terms with fields, thereby automating the zone identification and mapping process through a processor-driven interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual zone drawing is used to map text zones to fields, then zone mapping can be performed, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system performs preliminary actions by automatically detecting text zones and pre-defining zone boundaries before the user needs to map them to fields. This eliminates the need for users to manually draw zones, as the zones are already identified and ready for field association, significantly reducing time while maintaining accuracy
Solution Approach 2:
The system serves itself by automatically identifying text zones, determining their boundaries, and preparing them for field mapping without requiring manual user intervention. The automated zone detection and boundary definition processes handle the tedious drawing tasks, allowing users to focus only on the essential field association step
2Measurement precision
If users manually draw zones around text to identify positions, then text positioning can be captured, but the process becomes complex and difficult for end users
Solution Approach 1:
The system replaces the mechanical manual drawing process with automated computer-based zone detection algorithms. Instead of requiring users to physically draw zones with mouse movements, the system automatically detects and defines zone boundaries around text, significantly simplifying the operation while maintaining precise position identification
Solution Approach 2:
The system introduces an intermediary automated zone detection process between the user and the text positioning task. This intermediary automatically handles the complex boundary detection and zone definition, presenting simplified zone options to users for field mapping without exposing them to the underlying complexity of position identification
3Measurement precision
If multiple zones are drawn close to each other to capture detailed text, then comprehensive text coverage is achieved, but it becomes difficult for users to identify boundaries of different zones
Solution Approach 1:
The system creates simplified digital representations (copies) of text zones with clearly defined boundaries. Instead of requiring users to visually identify boundaries among closely spaced drawn zones, the system generates structured zone data with explicit boundary coordinates, making it easy to distinguish and map each zone to its corresponding field
4Illumination intensity
If zooming and switching views is required to view document images clearly, then image visibility is improved, but the workflow becomes interrupted and less efficient
Solution Approach 1:
The system transitions from a two-dimensional visual inspection approach (requiring zooming and panning) to a structured data representation in a different dimension. Zones are detected and defined programmatically, allowing users to work with zone data and boundaries without continuously manipulating the visual display, maintaining both visibility and workflow efficiency
Data Source
AI summary
Provided are techniques for dynamically mapping zones. For a file and a field, while receiving text characters, possible terms for the text characters are provided by querying an index. In response to receiving selection of a possible term from the possible terms, each occurrence and position of the selected term in the file is determined. In response to selection of an occurrence and position of the selected term, the file is marked to identify the selected occurrence at the position in the file. The selected term and the position are associated with the field for any other files that match a type of the file.