XML OCR Model Fine-Tuning for Accurate Document Reconstruction
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Solution Overview
Problem
Existing optical character recognition (OCR) services are inadequate for converting XML documents into machine-readable text due to the presence of special characters and markup patterns that do not follow natural English grammar, leading to inaccurate character identification and direction determination, which can result in invalid or erroneous XML documents.
Innovation Solution
A fine-tuned OCR model comprising a text detection model, bounding box direction classification model, text recognition model, and word splitter model is developed to accurately extract XML content from physical documents by generating and fine-tuning machine learning models using synthetic XML data and adjusting neural network weights for XML content extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing OCR services are used to convert XML documents into machine-readable text, then the conversion process can be performed, but the character identification and direction determination become inaccurate due to special characters and markup patterns
Solution Approach 1:
The patent applies preliminary action by pre-training the OCR model using synthetic XML documents that contain various special characters and markup patterns before actual XML conversion tasks. This pre-training prepares the model in advance to handle the unique characteristics of XML content, improving character identification accuracy and direction determination for XML-specific elements.
Solution Approach 2:
The patent changes the training parameters and data characteristics by using synthetic XML data with diverse fonts, sizes, and markup patterns. This parameter adjustment allows the OCR model to adapt to the specific visual and structural properties of XML documents, resolving the contradiction between maintaining general OCR functionality and achieving specialized XML recognition accuracy.
2Measurement precision
If a fine-tuned OCR model is developed using synthetic XML data and multiple machine learning models, then character recognition and direction alignment accuracy improve, but the system complexity increases
Solution Approach 1:
The patent segments the OCR task into multiple specialized machine learning models, each handling specific aspects such as text detection, direction classification, and character recognition. This segmentation allows each model to be optimized for its specific function, improving overall measurement precision while managing complexity through modular design.
Solution Approach 2:
The patent creates a multi-functional OCR system where a single fine-tuned model architecture serves multiple purposes: detecting text, determining direction, and recognizing characters. This universal approach reduces overall system complexity compared to having entirely separate systems for each function, while still achieving high precision through specialized training.
3Reliability
If multiple machine learning models are trained using synthetic training data, then XML content extraction accuracy improves, but the training time and computational resources increase
Solution Approach 1:
The patent uses synthetic XML documents as copied training data that replicates the visual and structural characteristics of real XML content without requiring actual physical documents. This copying approach enables efficient model training by generating unlimited training samples programmatically, reducing the time and resources needed compared to manual data collection while maintaining extraction accuracy.
Data Source
AI summary
Techniques are disclosed for optical character recognition of extensible markup language content. A method can include a system generating a first training data comprising extensible markup language (XML) content, the first training data comprising a first plurality of training instances, each training instance including a respective image comprising XML content and annotation information for the respective image. The system can train a plurality of machine learning models using the first training data to generate a plurality of trained machine learning models, to perform image-based XML content extraction. The system can generate a plurality of trained machine learning models based at least in part on the training.