Synthetic Document Pipeline for Diverse AI Training Data
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Solution Overview
Problem
Obtaining robust training data sets for deep learning models in document understanding tasks is challenging due to limited publicly available data, applicability issues, licensing restrictions on privately-held data, labor-intensive labeling, and the need for varied data sets with different labels and annotations.
Innovation Solution
A unified synthetic document generation pipeline that automates the creation of large volumes of training data with controlled content, format, and augmentations, while checking for biases and generating labels/annotations, reducing dependency on third-party vendors and manual processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If synthetic document generation pipeline is implemented, then productivity of training data generation is improved, but device complexity increases
Solution Approach 1:
The synthetic document generation pipeline is divided into distinct modular components: document structure generator, element generator, metadata generator, and augmentation module. Each component handles specific aspects of document creation independently, enabling parallel processing and reducing overall system complexity while maintaining high productivity.
Solution Approach 2:
The pipeline pre-generates document templates, element libraries, and metadata schemas before actual document creation. These pre-prepared resources are stored and reused during training data generation, significantly improving productivity without proportionally increasing runtime system complexity.
2Adaptability or versatility
If diverse document formats and augmentations are generated, then adaptability of training data is improved, but manufacturing precision control becomes more difficult
Solution Approach 1:
The system controls document generation precision by managing parameters such as document template identifiers, element types, metadata schemas, and augmentation factors. These parameters enable diverse output while maintaining reproducible and controllable generation processes through structured configuration.
3Measurement precision
If manual labeling and annotation is performed, then measurement precision of training data is improved, but loss of time increases
Solution Approach 1:
The synthetic document generation pipeline automatically generates documents with embedded metadata, labels, and annotations without requiring manual intervention. The system self-services the labeling process by incorporating ground truth information directly into the generated documents during the synthesis phase, eliminating time-consuming manual annotation while maintaining precision through controlled generation parameters.
Data Source
AI summary
Embodiments described herein are directed towards a synthetic document generation pipeline for training artificial intelligence models. One embodiment includes a method including a device that receives an instruction to generate a document to be used as a training instance for a first machine learning model, the instruction including an element configuration, a document class configuration, a format configuration, an augmentation configuration, and data bias and fairness. The device can receive an element from an interface based at least in part on the element configuration, the element can simulate a real-world image, real-world text, or real-world machine-readable visual code. The device can generate metadata describe a layout for the element on the document based on the document class configuration. The device can generate the document by arranging the element on the document based on the metadata, wherein the document is generated in a format based on the format configuration.