Custom neural document model

This article applies to: Form Recognizer v3.0 checkmark Form Recognizer v3.0.

Custom neural document models or neural models are a deep learned model type that combines layout and language features to accurately extract labeled fields from documents. The base custom neural model is trained on various document types that makes it suitable to be trained for extracting fields from structured, semi-structured and unstructured documents. The table below lists common document types for each category:

Documents Examples
structured surveys, questionnaires
semi-structured invoices, purchase orders
unstructured contracts, letters

Custom neural models share the same labeling format and strategy as custom template models. Currently custom neural models only support a subset of the field types supported by custom template models.

Model capabilities

Custom neural models currently only support key-value pairs and selection marks and structured fields (tables), future releases include support for signatures.

Form fields Selection marks Tabular fields Signature Region
Supported Supported Supported Unsupported Supported 1

1 Region labels in custom neural models use the results from the Layout API for specified region. This feature is different from template models where, if no value is present, text is generated at training time.

Build mode

The build custom model operation has added support for the template and neural custom models. Previous versions of the REST API and SDKs only supported a single build mode that is now known as the template mode.

Neural models support documents that have the same information, but different page structures. Examples of these documents include United States W2 forms, which share the same information, but may vary in appearance across companies. For more information, see Custom model build mode.

Language support

  1. Neural models now support added languages in the 2023-02-28-preview API.
Languages API version
English 2022-08-31 (GA), 2023-02-28-preview
German 2023-02-28-preview
Italian 2023-02-28-preview
French 2023-02-28-preview
Spanish 2023-02-28-preview
Dutch 2023-02-28-preview

Tabular fields

With the release of API versions 2022-06-30-preview and later, custom neural models will support tabular fields (tables):

  • Models trained with API version 2022-08-31, or later will accept tabular field labels.
  • Documents analyzed with custom neural models using API version 2022-06-30-preview or later will produce tabular fields aggregated across the tables.
  • The results can be found in the analyzeResult object's documents array that is returned following an analysis operation.

Tabular fields support cross page tables by default:

  • To label a table that spans multiple pages, label each row of the table across the different pages in a single table.
  • As a best practice, ensure that your dataset contains a few samples of the expected variations. For example, include samples where the entire table is on a single page and where tables span two or more pages.

Tabular fields are also useful when extracting repeating information within a document that isn't recognized as a table. For example, a repeating section of work experiences in a resume can be labeled and extracted as a tabular field.

Supported regions

As of October 18, 2022, Form Recognizer custom neural model training will only be available in the following Azure regions until further notice:

  • Australia East
  • Brazil South
  • Canada Central
  • Central India
  • Central US
  • East Asia
  • East US
  • East US2
  • France Central
  • Japan East
  • South Central US
  • Southeast Asia
  • UK South
  • West Europe
  • West US2
  • US Gov Arizona
  • US Gov Virginia


You can copy a model trained in one of the select regions listed to any other region and use it accordingly.

Use the REST API or Form Recognizer Studio to copy a model to another region.

Best practices

Custom neural models differ from custom template models in a few different ways. The custom template or model relies on a consistent visual template to extract the labeled data. Custom neural models support structured, semi-structured, and unstructured documents to extract fields. When you're choosing between the two model types, start with a neural model, and test to determine if it supports your functional needs.

Dealing with variations

Custom neural models can generalize across different formats of a single document type. As a best practice, create a single model for all variations of a document type. Add at least five labeled samples for each of the different variations to the training dataset.

Field naming

When you label the data, labeling the field relevant to the value improves the accuracy of the key-value pairs extracted. For example, for a field value containing the supplier ID, consider naming the field "supplier_id". Field names should be in the language of the document.

Labeling contiguous values

Value tokens/words of one field must be either

  • Consecutive sequence in natural reading order without interleaving with other fields
  • In a region that don't cover any other fields

Representative data

Values in training cases should be diverse and representative. For example, if a field is named "date", values for this field should be a date. synthetic value like a random string can affect model performance.

Current Limitations

  • The model doesn't recognize values split across page boundaries.
  • Custom neural models are only trained in English. Model performance is lower for documents in other languages.
  • If a dataset labeled for custom template models is used to train a custom neural model, the unsupported field types are ignored.
  • Custom neural models are limited to 10 build operations per month. Open a support request if you need the limit increased.

Training a model

Custom neural models are only available in the v3 API.

Document Type REST API SDK Label and Test Models
Custom document Form Recognizer 3.0 Form Recognizer SDK Form Recognizer Studio

The build operation to train model supports a new buildMode property, to train a custom neural model, set the buildMode to neural.


  "modelId": "string",
  "description": "string",
  "buildMode": "neural",
    "containerUrl": "string",
    "prefix": "string"

Next steps

Learn to create and compose custom models: