Synthetic data is training data that is generated rather than collected. Images can be rendered from a physical model of a scene and sensor. Documents can be built from a generated record and rendered into the layout of the document they imitate. Teams use it when the data they need is rare, private, unlabeled, expensive to collect, or does not exist yet.
How synthetic image data is made
Synthetic image data is made either by simulating a scene and its sensor, or by a generative model trained on real images. Only simulation produces exact ground truth.
Simulated data has one job: to work on real sensors. A model trained on simulation and deployed on real hardware can lose accuracy, and that drop is the sim-to-real gap.
Physics-based rendering is what separates a simulation from a good-looking picture. Lighting, distance, materials, and object motion are physically correct, so the frame matches what the real sensor would record. The gap closes by matching the physics, not by making prettier renders.
A model does not see an image the way a person does. It learns statistical patterns in pixel values. When simulated pixels differ systematically from the output of the real camera, the model learns patterns that do not hold up in deployment.
The important differences are physical:
Light transport: how light bounces, scatters, and creates shadows throughout the scene.
Material reflectance: how materials such as metal, plastic, glass, and fabric reflect light differently.
Lens distortion: the geometric warping a particular lens introduces, strongest near the edges of the frame.
Sensor noise: the grain, color shifts, and dynamic-range limits associated with a specific sensor.
A render can look photorealistic to a person and still get all four wrong. Another render can look relatively plain while modeling all four accurately enough to transfer well.
Domain randomization is a related technique. It varies lighting, textures, positions, and other conditions widely so the model learns what stays constant. It can work on its own, even with simple renders. Paired with accurate physics, it has less of the gap left to cover.
Simulation starts with a virtual version of the environment the model will encounter. Engineers build a 3D scene with the relevant objects, geometry, materials, surfaces, and backgrounds. They then configure the lighting and the virtual sensor: camera position, lens, resolution, field of view, and sensor behavior.
From there, the generator varies the conditions the model needs to learn. Objects can change position and orientation. Lighting, materials, backgrounds, camera angles, and environmental conditions can change from one frame to the next. Rare or dangerous events can be placed deliberately instead of waiting for them to happen in front of a real camera.
A physics-based renderer calculates how light interacts with the scene and what the configured sensor would record. The system knows the exact geometry and location of everything in the scene. That lets it produce the training image and its ground truth from the same scene state. Depending on the application, outputs can include RGB images, segmentation masks, depth maps, object poses, and simulated LiDAR (laser-based distance measurement). Image outputs stay pixel-aligned because they come from the same virtual moment, and simulated LiDAR stays registered to the camera for the same reason. Together they form a correlated multimodal dataset: several data types describing one moment and guaranteed to agree.
Generative image models work differently. They are trained on existing images. That category includes GANs (generative adversarial networks, two networks trained against each other) and diffusion models. Diffusion models build an output by removing noise step by step. The output can look convincing, but appearance is not correctness. These models have no model of light, materials, or optics to work from. They imitate the statistics of the images they trained on, so physical correctness is never guaranteed and cannot be verified. A shadow can point the wrong way, with no reliable label showing where the object’s edge actually belongs.
Augmentation is not synthetic data either. When you flip, crop, rotate, or add noise to an existing image, you create another version of a sample you already had. That can help a model handle small variations, but it does not introduce a new scene. If the original dataset never contained a condition, augmentation cannot invent the missing information.
For a closer look at the problem, read The Sim-to-Real Gap Explained and Simulation vs. Generative Synthetic Data.
How synthetic document data is made
Synthetic document data is made by generating field values, placing them on a page, and labeling the result. Methods differ in how coherent the values are.
The simplest approach is a random field generator such as Faker. It fills each field independently: a name here, an address there, a number after that, with nothing connecting them. LLMs can write field values or whole documents, and they bring more variety. Their output drifts across a large dataset, and it can include values that do not exist. Template renderers such as SynthDoG paste text into document layouts to pretrain document reading models, without labeling what each value means. Some teams skip generation and de-identify real documents, which leaves re-identification risk in play.
The record-first approach starts one level up. It generates the entity a document describes, a person, a household, or a vendor, with attributes that hold together. A schema for each document type then sets which fields appear, what values they accept, and how they relate. The page is rendered last, from the finished record, and that order is what the next three properties depend on.
The values are statistically grounded. Income, filing patterns, and demographic attributes follow real-world statistical structure instead of being picked at random. Cross-field dependencies hold: occupation drives income, income drives tax bracket, and address drives state filing requirements. Addresses come from U.S. Census Bureau geographic data, so street, city, state, and ZIP code line up.
Identities stay coherent. One synthetic person keeps the same name, address, and identifiers across every form generated for them. That holds whether the set is a 1040, a CMS-1500, or an I-9. A model trained on that data learns how real records connect.
Ground truth is exact. The generator renders the record into a layout, so it knows where every value sits on the page. It exports field values and entity classes from the record, and bounding boxes from the layout it drew. One system produces the page and the labels, so they cannot disagree.
Each of the other methods gives up at least one of those properties. Random field generators give up coherence. LLM output gives up consistency at scale: an invoice can look right while its line items fail to sum to the total. Template renderers give up semantic labels. De-identified real documents give up the privacy guarantee, and hand-labeled ones carry annotator error as well.
Synthetic images and synthetic documents solve different problems
Physics-based synthetic images are grounded in sensor and material physics. Synthetic documents are grounded in coherent records and real document layouts.
Synthetic images are used to train perception models that detect, segment, and measure objects from camera or sensor input. The useful question is not whether an image looks impressive to a person. It is whether the sample behaves like a capture from the real sensor.
A useful image dataset can provide several aligned outputs at once. Those include RGB, segmentation masks (a label for every pixel identifying its object), depth, and LiDAR.
Synthetic documents train extraction, OCR (optical character recognition), NLP, and fraud detection models that read invoices, forms, and records. When each document is built from a statistically grounded, internally consistent record, the labels describe exactly what is on the page. For more on these use cases, see synthetic document data on SymageDocs.
Some applications need both. A logistics company might train one model to read package labels from a conveyor camera and another to extract fields from shipping paperwork. Those are separate data problems. One depends on sensor physics; the other depends on document structure. Each needs a generator built for the job.
When synthetic data beats real data
Synthetic data is most useful when the data you need is rare, dangerous, private, or expensive to label.
Rare events and class imbalance. Some categories appear far less often than others in a training set. That is class imbalance, and it can leave a model unprepared for the cases that matter most. A defect that appears once in every ten thousand parts will barely register during training. A near-miss on a factory floor may never be captured at all. On the document side, an altered invoice or an unusual form variant may turn up in a handful of files out of millions. With synthetic data, you can generate those tail cases in the volume the model needs.
Privacy and compliance constraints. Medical records, financial documents, and identity documents often cannot leave a secure environment. Approval to use them can take months, if it comes at all. Generated data with no real person’s information can often move through the pipeline with far less review.
Annotation cost. Manually labeling segmentation masks, depth, or document fields takes time, costs money, and introduces human error. Different annotators may also interpret the same sample differently. Purpose-built synthetic data arrives with labels because the generator already knows what it created.
Iteration speed. Suppose a deployed model fails under glare, or meets a vendor invoice in a layout it has never seen. You can generate targeted examples of that condition in days. Collecting and labeling the equivalent real-world data can take an entire quarter.
When the target domain is common, cheap to capture, and already labeled, real data may be all you need. More often, teams end up with a hybrid set. Real data covers the conditions the model sees every day. Synthetic data fills in what is missing: the rare, risky, private, or unlabeled cases.
What ground truth comes with synthetic datA
Every sample from a physics-based or record-based generator arrives labeled. The generator produces the data and its ground truth in one step, so nothing is annotated by hand.
For images, the key is pixel alignment. The RGB frame, segmentation mask, and depth map describe the same point at the same pixel. That is what makes the outputs a correlated multimodal dataset rather than separate files that roughly match.
That level of agreement is difficult to reproduce with hand-labeled data. Each modality may be labeled separately, sometimes by different people, which creates opportunities for inconsistency.
For documents, ground truth includes field values, each field’s position on the page, and the entity each field represents, such as a vendor name. The values and entities come from the record; the positions come from the renderer that placed them. Because one system does both, the labels describe what is actually on the page, including values that are truncated or partly masked.
Exact ground truth matters for evaluation as much as it does for training. When the test labels are reliable, the error you measure belongs to the model rather than the annotator. You can also generate cases around one specific condition and see precisely where the model starts to fail.
How to evaluate a synthetic dataset
The real test of a synthetic dataset is how a model trained on it performs in the field. Whether the samples look realistic is secondary.
Before committing to a dataset or vendor, run through four checks. All four are possible on a sample dataset, provided you have a small set of labeled real data and the generation parameters behind the samples.
1. Run a transfer test. Train a model on the synthetic data, then evaluate it on held-out real data it has never seen. The test set can be small, but it has to be real and labeled. If you have enough real data to train on as well, compare the two. This is the most important measurement.
2. Audit label accuracy. Spot-check the labels against the samples. A correctly configured generator produces exact labels, but configuration errors can still happen. A small audit can catch them before they affect training.
3. Check tail coverage. Ask for the generation parameters or a manifest. Confirm the dataset holds the rare cases you asked for, in the volume you asked for. Tail coverage is usually the reason for using synthetic data in the first place.
4. Match the sensor or format. For images, ask which sensor profile was used, then check resolution, lens, and noise against your own captures. For documents, compare layouts and value distributions against samples from production.
The fastest way to make those checks is to work with data you can inspect and test yourself. Download a sample dataset, train with it, and measure performance against your own real images.
Frequently asked questions
What is synthetic data?
It is data generated rather than collected from the world. When a generator builds it from a model of a scene or a record, the data and its labels are created together.
Is synthetic data the same as data augmentation?
No. Augmentation transforms existing samples. Synthetic data creates new samples with new information, and the labels come with them when a generator built the sample from a model.
Is synthetic data the same as AI-generated data?
Not necessarily. Generative AI is one way to create it. Simulating a scene, or generating a record and rendering it, is another. Those methods produce exact ground truth because the generator creates both the sample and its labels.
Can a model trained only on synthetic data work in the field?
It can when the synthetic data closely matches the deployment sensor or document format. Most teams get the best results by combining synthetic data with a smaller real dataset.
How much synthetic data do I need?
Enough to cover the cases missing from your real data. Coverage of the tail matters more than total volume.
Does synthetic data contain personal information?
Synthetic records are designed not to contain real personal information, because people, organizations, and values are generated rather than copied. Ask how a dataset was made, since generative models trained on real records can leak training details.
How do I know whether a synthetic dataset is any good?
Train on it, test on held-out real data, and compare the results. Transfer is the measure, not how realistic the samples look.
What is the difference between synthetic images and synthetic documents?
Physics-based synthetic images are rendered from sensor and material physics for perception models. Synthetic documents are generated from a record and rendered into a layout for extraction, NLP, and fraud detection models. They are different kinds of data, made by different pipelines.