Build and optimize a custom model for Azure Form Recognizer

Building and optimizing a custom model for Azure Form Recognizer is an essential aspect of designing and implementing a Microsoft Azure AI solution. Azure Form Recognizer is a powerful service that enables businesses to extract valuable information from forms, invoices, receipts, and other documents with ease. In this article, we will explore the steps involved in building and optimizing a custom model using Azure Form Recognizer.

Before You Begin

To get started, you need to have an Azure subscription and create a Form Recognizer resource in the Azure portal. Once you have the resource in place, you can proceed with building and optimizing the custom model.

Step 1: Data Preparation

The first step in building a custom model is to gather and prepare the training data. The training data should consist of a sufficient number of representative documents that cover a range of variations and scenarios. Make sure the data is labeled correctly to facilitate supervised learning. Azure Form Recognizer supports two methods for labeling data: key-value pairs and tables.

Step 2: Training the Model

Once the training data is ready, you can use the Form Recognizer API or the Azure portal to train the custom model. The training process involves submitting the labeled data to the service and waiting for the model to be trained. The duration of the training process can vary depending on the size of the dataset and the complexity of the documents.

To train the model using the Azure portal:

1. Navigate to your Form Recognizer resource in the Azure portal.
2. Select the “Custom Models” tab.
3. Click on the “New training” button.
4. Provide a name for the training run, select the labeled training data, and start the training process.
5. Monitor the training status and progress in the portal.

Step 3: Evaluating Model Performance

After the training is complete, it is crucial to evaluate the performance of the trained model. Azure Form Recognizer provides metrics such as accuracy, precision, and recall to assess the model’s performance. These metrics help you understand how well the model is extracting information from the documents.

Step 4: Iterative Refinement

In most cases, the first iteration of the model may not meet all the requirements, and further refinement is necessary. Azure Form Recognizer allows you to iterate and improve the performance of the model by providing additional training data and retraining the model. This iterative process helps in optimizing the model to achieve better accuracy and reliability.

Step 5: Extracting Information

Once the model is trained and optimized, you can use it to extract information from new documents. Azure Form Recognizer provides APIs and SDKs that can be integrated into your applications or workflows to extract data fields, tables, and other information from the documents automatically. The extracted information can then be processed and used for further analysis or automation.

To enhance the extraction accuracy, you can also provide additional hints or labeling information during the document analysis process. This helps the model to understand the structure and context of the documents more effectively.

Conclusion

Building and optimizing a custom model for Azure Form Recognizer is a crucial step in designing and implementing an Azure AI solution. By following the steps mentioned above and utilizing the capabilities of Azure Form Recognizer, you can create a powerful and accurate document processing system that can automate data extraction and drive operational efficiency within your organization.