Privacy concerns are a pre-requisite when relying on real-world documents for AI training. As we try to eliminate these concerns in computer vision systems, particularly for use cases like identification, financial and medical record management, Steve Harris, CEO of Mindtech, shares how synthetic documents offer an efficient and privacy-compliant solution.
The AI space is constantly evolving with new technologies emerging and a swift pace of adoption. The data-centric nature of the world makes changes in data practices paramount for any business to monitor, especially if they wish to keep up in the age of rapid digital transformation.
Real-world data has always been key for AI model training, but relying on it alone comes with unavoidable issues. This makes the need for a more optimal solution highly apparent. Naturally, a faster, more affordable and privacy-compliant solution has come to light â€“ synthetic dataOpens a new window .Â
What’s Brought on the Surge of Synthetic Training Data?
Synthetic training data is artificially generated by computer systems and recently its usage has been on the rise. In fact, Gartner estimates that by 2030, synthetic data will completely overshadow real data in AI modelsOpens a new window . Largely driven by it massively reducing the need to acquire and manually annotate real-world data sets, synthetic training data cuts the time of training and in particular, iterating an algorithm considerably. Essentially, synthetic data offers a quicker, more cost-effective method of generating masses of data that are not subject to the privacy concerns of real data.
Synthetic data doesn’t come in one form. There is a range of synthetic data, including: tabular, text, videos, images and sound. One of the first mainstream uses of synthetic training data was that of â€œtabular dataâ€ (e.g., bank records, user analytics, etc.). Intrinsically, this is artificially generated data stored in tables and used to train AI models for database analysis. Building on this, synthetic documents pull together tabular synthetic documentation alongside visual synthetic data to mimic real-world documents for a range of use cases.
What Are the Benefits of Synthetic Documents?
Synthetic documents can be used to train machine learning models for tasks such as document classification, language translation, and text summarisation, to name a few, all of which rely on accurate optical character recognition (OCR). This serves the purpose of protecting privacy by removing the need for identifiable real-world data, which can also be expensive and labor-intensive to collect. Not only that, but synthetic documents can be generated in large quantities as required to ensure that training sets are robust and diverse enough to deal with corner cases.
In real-world contexts, documents can undergo inevitable wear and tear. Vision systems need to be able to account for these various cases and environmental factors, emphasizing the need for a synthetic solution that utilizes 3D modeling to allow for the generation of the diverse environments in which these documents may be found. It will not always be the case that an ID is flat scanned and so, being able to accurately model light, shadows and reflections in a room will significantly improve the robustness of AI vision systems.Â
By leveraging 3D modeling, users can create accurate samples of creases, folds and damage to documents, all of which is typically expected when encountering real examples, in a move to significantly improve performance. Therefore, synthetic documents equip AI vision systems with the range of key aspects necessary to build reliability for corner cases and can do so rapidly as you are not reliant on real-world sources.Â
In certain cases, synthetic documents can also be used to protect sensitive information as it eliminates the need to use personally identifiable information (PII). This is particularly critical when it comes to identification and financial and medical record management, areas where privacy is of the utmost importance. Without the need to use information traceable to individuals, synthetic documents are able to alleviate PII concerns. And in turn, it can avoid the accompanying risk of deploying models that violate privacy regulations.
Synthetic documents can also be used to test machine learning models in a controlled environment, reducing the risks associated with real-world testing. These can be generated with specific characteristics in mind such as language, format, and content, allowing for fine-tuned training. It enables users to control and balance the distribution of classes in the dataset, which can also be easily augmented and modified, allowing for more efficient model iteration and testing, in addition to helping mitigate bias in the model.
Industries that Can Benefit from Synthetic Documents
Healthcare and banking are just a couple of industries that can benefit from leveraging synthetic documents. Any space where data is difficult to collect due to privacy and security issues can benefit. For example, insurance claims and policies, customer service transcripts, and legal documents. In banking, financial statements such as balance sheets, income statements, and cash flow statements are subject to security concerns. Synthetic documents can be used to train models for tasks such as financial forecasting and risk assessment.
Notably, synthetic documents are driving increases in OCR accuracy, where even marginal improvements are highly valuable. This is especially important in cases such as remote identification of ID documents via scanning, where discrepancies can have serious consequences. Similarly, medical records such as patient charts, lab results, and discharge summaries cannot afford to be vulnerable to erroneous results. Synthetic documents can be deployed to train models for tasks such as medical coding, disease diagnosis, and patient treatment planning, cases where enhanced accuracy can drive impactful changes in healthcare.
From scanning ID for remote identification purposes to fraud detection and claims processing, there is a whole range of applications requiring documents to be used as training data. With technological advancement and innovation, synthetic data is becoming richer, more diverse, and closely aligned to real-world data.Â
Synthetic documents complement real-world documents for training models, while providing the significant benefits of artificially generated data. It provides robust and versatile datasets for AI training purposes eliminating the need for manual efforts, and so, is quicker, comprehensive and more cost-effective to gather. Most critically, synthetic documents also alleviate the inherent security and privacy concerns associated with real documents. It’s about using one as a supplement to the other to optimize machine learning models and have the best of both worlds.