Document Key Position Alignment for Accurate Data Extraction
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Solution Overview
Problem
Existing text extraction techniques struggle to accurately extract relevant information from complex documents lacking predefined formats or tags, making it difficult to verify the correctness of extracted data.
Innovation Solution
A method and system utilizing a deep learning model and optical character recognition (OCR) combined with natural language processing (NLP) to determine positional information of predefined keys in document images, integrating user-defined mapping information and predefined rules to ensure data extraction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCR techniques are used to extract text from documents, then text data can be extracted from images or PDF files, but the extracted data from complex documents without predefined formats or tags cannot be verified for correctness
Solution Approach 1:
The system employs a feedback mechanism where extracted data is validated against predefined templates and patterns. The NLP model compares extracted text with expected formats and provides feedback to correct or reject inaccurate extractions, ensuring data correctness verification is possible even from unstructured documents.
Solution Approach 2:
An intermediary validation layer is introduced between OCR extraction and final data output. This layer uses predefined templates and NLP models to mediate the verification process, checking extracted data against expected patterns and structures before final output, thus enabling correctness verification without requiring predefined document formats.
2Measurement precision
If deep learning models are used to determine positional information, then extraction precision can be improved, but the system complexity increases
Solution Approach 1:
The system segments the data extraction process into distinct modules: a deep learning model for positional information detection, an NLP model for text extraction and validation, and a template-based verification system. This segmentation allows each component to specialize in specific tasks, improving overall precision while managing complexity through modular architecture.
Solution Approach 2:
The system uses a multi-functional approach where the deep learning model handles positional information extraction, the NLP model performs text extraction and format validation, and predefined templates provide structure guidance. This universality allows a single integrated system to handle multiple functions (extraction, validation, verification) without proportionally increasing complexity.
3Reliability
If multiple extraction approaches are combined, then data extraction reliability improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by first using the deep learning model to identify potential positional information and extract candidate text regions. This preliminary extraction is then quickly validated by the NLP model against predefined templates, allowing reliable data extraction while minimizing processing time through early filtering and prioritization of high-confidence extractions.
Data Source
AI summary
A method and system for relevant data extraction from a document is disclosed. The method includes determining first positional information corresponding to a key from a plurality of predefined keys in the document image based on a deep learning model. Further, second positional information corresponding to the key is determined based on OCR of the document image and an NLP model. Final positional information is determined based on the first positional information and the second positional information, in case a difference between the first positional information and the second positional information is minimal. Relevant data is extracted for the key in the OCR document image based on the final positional information.