Document ML Rendering Using Content Order for Accurate Classification
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Solution Overview
Problem
Existing machine learning models often misclassify or wrongly detect objects within documents due to the lack of consideration for content order, leading to inaccuracies in detection and classification tasks.
Innovation Solution
Integrate content order scores or values as an input feature for machine learning models to improve accuracy by reflecting the natural language reading order of instances within documents, using a set of rules to generate and normalize these scores, and superimpose them as values within the document for better instance detection and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models process documents without content order information, then processing speed is maintained, but classification and detection accuracy deteriorates
Solution Approach 1:
The system performs preliminary processing to generate content order scores and values before the machine learning model processes the document. This pre-computation of spatial relationships and reading order information is done outside the main model processing pipeline, so when the model receives the data with embedded content order values, it can make more accurate predictions without the computational burden of calculating these relationships during inference.
Solution Approach 2:
Content order values act as an intermediary that bridges the gap between raw document structure and model understanding. These values encode spatial relationships, reading order, and hierarchical structure in a format that the machine learning model can easily consume, improving classification accuracy without requiring the model to directly process complex spatial relationships.
2Measurement precision
If machine learning models use traditional processing without content order, then computing resource consumption is reduced, but detection accuracy deteriorates
Solution Approach 1:
Content order analysis is performed as a preliminary step before the main detection process. By pre-computing spatial relationships, reading order, and hierarchical structure, the system avoids the need for complex computational operations during the actual detection phase, thereby improving detection accuracy while keeping overall computing resource consumption manageable.
Solution Approach 2:
The system replaces complex mechanical processing within the machine learning model with a simpler computational approach using content order values. Instead of requiring the model to computationally infer spatial relationships and reading order from raw document structure, the pre-computed content order values provide this information directly, reducing the computational burden during detection.
3Measurement precision
If machine learning models process documents without content order values, then training data requirements are maintained, but prediction accuracy deteriorates
Solution Approach 1:
Content order values serve as an intermediary feature that enriches the training data by encoding spatial relationships, reading order, and hierarchical structure. This additional structured information helps the model learn more effective patterns from smaller datasets, improving prediction accuracy without requiring proportionally larger training volumes.
Solution Approach 2:
The system changes the parameter space by adding content order values as new input features. This transformation of the input data representation provides the model with additional meaningful dimensions for learning, enabling better predictions with less training data since the model doesn't need to infer these relationships from scratch during training.
4Reliability
If machine learning models ignore content order, then processing simplicity is maintained, but classification reliability deteriorates
Solution Approach 1:
The system performs preliminary computation of content order scores and values that capture spatial relationships, reading order, and hierarchical structure. This pre-processing step establishes a reliable foundation for classification by organizing document structure information before it reaches the classification stage, improving classification reliability without adding complexity to the core classification algorithm.
Solution Approach 2:
Content order values act as a reliable intermediary that provides structured spatial and hierarchical information to the classification process. By encoding reading order and spatial relationships in this intermediary representation, the system ensures consistent and reliable classification results without requiring the classification algorithm itself to become more complex.
Data Source
AI summary
Various disclosed embodiments can resolve output inaccuracies produced by many machine learning models. Embodiments use content order as input to machine learning model systems so that they can process documents according to the position or rank of instances in a document or image. In this way, the model is less likely to misclassify or incorrectly detect instances or the ordering between predicted instances. The content order in various embodiments can be used as an additional signal to classify or make predictions.