Machine Learning Model Training Data Generation for Document Metadata
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Solution Overview
Problem
Existing electronic document processing systems relying on machine learning require large amounts of labeled training data and human input, making the process tedious and prone to errors, especially when dealing with unstructured documents like scanned images.
Innovation Solution
A method that generates metadata for structured and unstructured electronic documents by comparing processed bitmaps with original metadata, using a machine learning model to iteratively refine metadata recognition, allowing for the conversion of uneditable documents into editable formats and improving metadata recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised machine learning is used with curated training data sets, then the machine learning model can achieve accurate document processing, but the process requires extensive human input and labeled training data making it tedious and time-consuming
Solution Approach 1:
The system uses the machine learning model to automatically generate and refine its own training data by processing structured electronic documents and extracting metadata, eliminating the need for manual human labeling. The model iteratively improves its own training set by converting structured documents to bitmaps and back, self-correcting metadata through comparison with original document properties.
Solution Approach 2:
The system pre-processes structured electronic documents by converting them to bitmaps and generating metadata before actual training begins. This preliminary generation of training data with known ground truth metadata allows the model to start training with pre-prepared data, reducing the time needed during the actual training phase.
2Reliability
If large sets of labeled training data are used, then the machine learning model becomes more effective, but the complexity and resource requirements of the training process increase
Solution Approach 1:
The system creates synthetic training data by copying and transforming existing structured electronic documents into bitmap format, then converting them back to structured format with extracted metadata. This copying process generates additional training examples without requiring manual creation, increasing data volume while maintaining consistency with the original document structure and reducing training complexity.
Solution Approach 2:
The system changes the representation parameters of documents by converting between structured format (with metadata) and bitmap format (image representation). This parameter transformation allows the same document to serve multiple training purposes and generates diverse training examples from a single source document, improving model effectiveness without proportionally increasing complexity.
3Measurement precision
If human input is required to indicate intended results, then the training data quality is high, but the process becomes tedious and prone to human error
Solution Approach 1:
The machine learning model automatically extracts metadata from bitmaps and generates training labels without human intervention. The system self-evaluates its own performance by comparing generated metadata with original document properties and iteratively refines its training data, eliminating manual human input while maintaining high training data quality through automated validation.
Solution Approach 2:
The system implements a feedback loop where the machine learning model's generated metadata is compared against the original structured document properties. This feedback mechanism automatically identifies discrepancies and uses them to refine the training data and improve the model, replacing manual human review with automated validation that reduces errors and increases ease of operation.
4Adaptability or versatility
If the system processes unstructured documents like scanned images, then the versatility of the document processing system increases, but the difficulty of metadata recognition and processing increases
Solution Approach 1:
The system uses structured electronic documents as an intermediary to train the machine learning model on unstructured documents. By first processing structured documents with known metadata to create training data, the model learns to extract metadata from bitmap representations. This intermediary training approach enables the model to subsequently process unstructured scanned images and other document formats with varying difficulty levels.
Solution Approach 2:
The system performs preliminary processing of structured documents to generate training data with known ground truth metadata before attempting to process unstructured documents. This preliminary training phase prepares the model to handle the increased difficulty of metadata extraction from unstructured formats by first learning from well-structured examples, thereby improving adaptability while managing processing difficulty.
Data Source
AI summary
A method is disclosed for training a machine learning model to process electronic documents (EDs). The method includes obtaining a structured ED (SED) from a document repository, where the SED includes a first metadata. The method further generates, based on the SED, a bitmap and a second metadata. The method also determines whether the second metadata is within a predetermined threshold of the first metadata and generates, based on the SED and in response to determining that the second metadata is not within the predetermined threshold of the first metadata, a third metadata. The method additionally determines whether the third metadata is within the predetermined threshold of the first metadata and stores, in response to determining that the third metadata is within the predetermined threshold of the first metadata, a second SED comprising the bitmap and the third metadata.