Build a training data set for a custom model

When you use the Form Recognizer custom model, you provide your own training data to the Train Custom Model operation, so that the model can train to your industry-specific forms. Follow this guide to learn how to collect and prepare data to train the model effectively.

If you're training without manual labels, you can use five filled-in forms, or an empty form (you must include the word "empty" in the file name) plus two filled-in forms. Even if you have enough filled-in forms, adding an empty form to your training data set can improve the accuracy of the model.

If you want to use manually labeled training data, you must start with at least five filled-in forms of the same type. You can still use unlabeled forms and an empty form in addition to the required data set.

Custom model input requirements

First, make sure your training data set follows the input requirements for Form Recognizer.

Form Recognizer works on input documents that meet these requirements:

  • Format must be JPG, PNG, PDF (text or scanned), or TIFF. Text-embedded PDFs are best because there's no possibility of error in character extraction and location.
  • File size must be less than 50 MB.
  • Image dimensions must be between 50 x 50 pixels and 10000 x 10000 pixels.
  • PDF dimensions must be at most 17 x 17 inches, corresponding to Legal or A3 paper sizes and smaller.
  • For PDF and TIFF, only the first 200 pages are processed (with a free tier subscription, only the first two pages are processed).
  • The total size of the training data set must be 500 pages or less.
  • If your PDFs are password-locked, you must remove the lock before submitting them.
  • If scanned from paper documents, forms should be high-quality scans.
  • Text must use the Latin alphabet (English characters).
  • For unsupervised learning (without labeled data), data must contain keys and values.
  • For unsupervised learning (without labeled data), keys must appear above or to the left of the values; they can't appear below or to the right.

Form Recognizer doesn't currently support these types of input data:

  • Complex tables (nested tables, merged headers or cells, and so on).
  • Checkboxes or radio buttons.

Training data tips

Follow these additional tips to further optimize your data set for training.

  • If possible, use text-based PDF documents instead of image-based documents. Scanned PDFs are handled as images.
  • For filled-in forms, use examples that have all of their fields filled in.
  • Use forms with different values in each field.
  • If your form images are of lower quality, use a larger data set (10-15 images, for example).

Upload your training data

When you've put together the set of form documents that you'll use for training, you need to upload it to an Azure blob storage container. If you don't know how to create an Azure storage account with a container, following the Azure Storage quickstart for Azure portal. Use the standard performance tier.

If you want to use manually labeled data, you'll also have to upload the .labels.json and .ocr.json files that correspond to your training documents. You can use the Sample labeling tool (or your own UI) to generate these files.

Organize your data in subfolders (optional)

By default, the Train Custom Model API will only use form documents that are located at the root of your storage container. However, you can train with data in subfolders if you specify it in the API call. Normally, the body of the Train Custom Model call has the following format, where <SAS URL> is the Shared access signature URL of your container:

  "source":"<SAS URL>"

If you add the following content to the request body, the API will train with documents located in subfolders. The "prefix" field is optional and will limit the training data set to files whose paths begin with the given string. So a value of "Test", for example, will cause the API to look at only the files or folders that begin with the word "Test".

  "source": "<SAS URL>",
  "sourceFilter": {
    "prefix": "<prefix string>",
    "includeSubFolders": true
  "useLabelFile": false

Next steps

Now that you've learned how to build a training data set, follow a quickstart to train a custom Form Recognizer model and start using it on your forms.

See also