README.md

Create Form Recognizer Machine Learning Model

Follow the steps below to train a form recognizer composite machine learning (ML) model. This also includes the steps to train a non-composite ML model.

Step 1: Locate the Sample Data Files

After finishing the deployment process described in Deployment Scripts Guide, your form recognizer resource should be ready for you to start creating new Form Recognizer ML model. From your cloned repository, navigate to the project root directory then to Deployment\Data\samples\train

You will see two folders that contain sample training forms, as illustrated below.

Step 2: Upload Sample Forms to Azure Data Lake Storage

1. Go to the Azure Portal and select the Azure Data Lake Storage Account that was created by deployment script.
2. On the left side of the menu pane, select Data storage, then Containers and go to the samples container.
3. Create a new folder and name it train.
4. In the train folder, create two folders. One named contoso_set_1 and the other named contoso_set_2.
5. Upload the sample labeling files in Data/samples/train/contoso_set_1 and Data/samples/train/contoso_set_2 into the corresponding folders. You now have two full sets of pre-labeled data to create the machine learning models.

Step 3: Train and Merge Form Recognizer ML Models

In this step you will train custom Azure Form Recognizer ML models and merge them into a composite model. For more information, please refer to Azure online document Compose Custom Models v3.0.

1. Go to Form Recognizer Studio, scroll down to Custom Model and select Create new, as illustrated below.

2. Select +Create a project to create a project.
3. Enter a project name. For example SafetyFormProject-Set-1 or any other project name of your choice.
4. Enter a project description. For example Custom form recognizer model with samples contoso_set_1 and click Continue.
5. Select your Subscription, Resource Group and Azure Form Recognizer created by the deployment scripts main-deploy.ps1.
6. Select the latest API Version. This solution was tested with API version 2022-08-31, as illustrated below. Click Continue.
7. Now you will be prompted to enter the training data source, as illustrated below. Select your subscription. Select Resource Group, and Azure storage created by the deployment scripts main-deploy.ps1. Enter samples in the Blob container field. Enter train/contoso_set_1 in the Folder path field. Click Continue.

8. Review Information and click Create Project. This step connects the form recognizer studio to Azure data lake storage/container resource in your subscription to access the training data.
9. After the project is created, forms with OCR, field key and value pair will appear as illustrated below. Click 'Train' on upper right corner,

10. Fill in information as below, and select the dropdown "Build Mode" to Template, and then click Train.

11. Once the training for contoso_set_1 samples is done, the model will be located in Models tab with confidence score of each field, as illustrated below.

12. Train second model with files stored in train/contoso_set_2, using above steps. Name your second model as consoto-set-2 or choice of your own. Click 'Models' from your project, you will see a list of models already created. You can now merge individual models into a composite model. Select contoso-set-1 and contoso-set-2, then click Compose. The system will prompt you for a new model name and description. Enter contoso-safety-forms or choice of your own, with a description. Click Compose.

13. Now your model id contoso-safety-forms will appear in the Model ID list, as illustrated below. Please make a note of this composed model id as you will need it to set up the Azure Functions App to use this model id, as described in Solution Configuration Guide.