ML Header Detection for Unstructured Document Data Extraction
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Solution Overview
Problem
Unstructured data in electronic documents, such as electronic spreadsheets, lacks a predefined format, making it difficult to collect, process, and analyze, particularly when mixed with structured data, as seen in remittances or payment advices containing invoice breakdowns.
Innovation Solution
A machine learning model is applied to identify and extract structured data by determining header rows in electronic documents, using string metrics to match cell content with entities, and extracting relevant data based on the identification of header fields, even in the presence of unstructured data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning model is applied to identify header rows in electronic documents containing unstructured data, then extraction accuracy of structured data is improved, but processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-processing the electronic document to identify and mark potential header rows before the main extraction process. The machine learning model is trained beforehand on sample data to recognize header patterns, enabling faster processing during actual data extraction while maintaining high accuracy.
Solution Approach 2:
The system applies partial action by focusing the machine learning model only on identifying header rows rather than processing the entire document structure. This selective approach reduces computational overhead while maintaining extraction accuracy for the critical header identification task.
2Measurement precision
If machine learning model is applied to identify header rows in electronic documents containing unstructured data, then extraction accuracy of structured data is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer - a pre-trained machine learning model that acts as a mediator between the unstructured document data and the extraction process. This intermediary handles the complex pattern recognition task, allowing the main extraction system to remain relatively simple while achieving high accuracy through the specialized ML component.
Solution Approach 2:
The system changes parameters by transforming the unstructured document into a structured format with identified header rows, converting qualitative text patterns into quantitative features that the machine learning model can process. This parameter transformation simplifies the overall system architecture by creating clear delineations between data sections.
3Measurement precision
If string metrics are used to match cell content with entities, then data extraction precision is improved, but computational resources required increase
Solution Approach 1:
The system applies local quality by using string metrics only for matching cell content with entities in header rows, rather than applying complex matching algorithms to the entire document. This localized application of precision matching reduces overall computational resource consumption while maintaining high extraction precision where it is most needed.
Data Source
AI summary
A method for analyzing an electronic document including structured data and unstructured data may include applying a machine learning model to determine whether one or more rows of the electronic document correspond to a header row. The machine learning model may be trained to determine whether one or more cells of a row corresponds to a header field by determining whether a text value included in the cells corresponds to an entity. A row may be identified as a header row based on an output of the machine learning model indicating more than a threshold quantity of cells included in the row correspond to a header field. At least a portion of the structured data included in the electronic document may be extracted based on the entity included in the cells of the row identified as the header row. Related systems and computer program products are also provided.