By Steve Harris, CEO, Mindtech Global
Artificial intelligence (AI) models rely on real-world data for training, but this approach comes with challenges such as privacy concerns, high costs, and time-consuming manual annotation. Synthetic data has emerged as a faster, more affordable, and privacy-compliant alternative for AI model training.
As the industry looks to move beyond ‘simple’ OCR to full deep semantic understanding of documents, synthetically generated documents, which combine tabular and visual synthetic data, can be particularly useful for training machine learning models for tasks such as document classification, language translation, and text summarisation. Consequently, this significantly streamlines processes like contract reviewing, for example.
What are the benefits of synthetic documents?
Synthetic documents offer a range of benefits that make them an attractive alternative to real-world data for AI model training. For one, synthetic documents protect privacy by removing the need for identifiable information (PII), which can be expensive and labour-intensive to collect. Synthetic documents can also be generated in large quantities as required to ensure that training sets are robust and diverse enough to deal with corner cases.
In addition, synthetic documents can be generated to mimic a range of real-world scenarios and environmental factors, improving the robustness of AI vision systems. By leveraging 3D modelling, synthetic documents can accurately simulate the creases, folds, and damage that can occur in real-world documents, improving performance and reliability. This approach can also help mitigate bias in the model, ensuring that the AI system is trained on a diverse and representative dataset.
Industries that can benefit from synthetic documents
Industries such as healthcare, legal, and banking can benefit from synthetic documents, particularly those concerned with protecting PII due to the sensitive nature of the data they collect and store. Privacy and security concerns can make it difficult to collect and use real-world data for AI model training. Synthetic documents offer a promising solution by providing an alternative source of data that can be used for tasks such as financial forecasting, risk assessment, medical coding, disease diagnosis, and patient treatment planning.
Synthetic documents can be used for remote identification of ID documents via scanning, fraud detection, and claims processing. By using a combination of synthetic and real-world data, AI models can be optimised for accuracy, speed, and reliability.
Many industries can benefit from synthetic documents, particularly in areas where data collection is difficult due to privacy and security concerns surrounding PII. The use of synthetic documents can provide a cost-effective and compliant way to generate large and diverse datasets that can be used to train AI models for various tasks. Most critically, synthetic data alleviates the inherent security and privacy concerns associated with real documents. It’s about using one as a supplement to the other to optimise machine learning models and have the best of both worlds.
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How data privacy concerns can become a thing of the past with synthetic documents was originally published in MindtechGlobal on Medium, where people are continuing the conversation by highlighting and responding to this story.