Azure Document Intelligence

https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence
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Azure Document Intelligence is a cloud-based machine learning service that automates data processing...
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### Check cURL installation

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/how-to-guides/use-sdk-rest-api

Run this command to verify that cURL is installed on your system. If the help options display, cURL is installed. This is a prerequisite for using the REST API examples.

```console
curl -help
```

--------------------------------

### Import libraries for Document Intelligence quickstart

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/quickstarts/get-started-sdks-rest-api

Imports the required libraries for the quickstart, including os, FormRecognizerClient, and AzureKeyCredential.

```python
import os
from azure.ai.formrecognizer import FormRecognizerClient
from azure.core.credentials import AzureKeyCredential
```

--------------------------------

### Console: Run doc_intel_quickstart.py

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/quickstarts/get-started-sdks-rest-api

Run the application from the folder containing doc_intel_quickstart.py. This command executes the layout analysis sample.

```Console
python doc_intel_quickstart.py

```

--------------------------------

### Run the application (Python)

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/quickstarts/get-started-sdks-rest-api

Command to run the Python quickstart script from the terminal. Assumes the script is named doc_intel_quickstart.py in the current directory.

```bash
python doc_intel_quickstart.py
```

--------------------------------

### Get Custom Model API - JSON response

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/how-to-guides/use-sdk-rest-api

Example JSON response from the Get Custom Model API, containing modelInfo, keys, and trainResult details.

```json
{
  "modelInfo": {
    "modelId": "string",
    "status": "creating",
    "createdDateTime": "string",
    "lastUpdatedDateTime": "string"
  },
  "keys": {
    "clusters": {}
  },
  "trainResult": {
    "trainingDocuments": [
      {
        "documentName": "string",
        "pages": 0,
        "errors": [
          "string"
        ],
        "status": "succeeded"
      }
    ],
    "fields": [
      {
        "fieldName": "string",
        "accuracy": 0.0
      }
    ],
    "averageModelAccuracy": 0.0,
    "errors": [
      {
        "message": "string"
      }
    ]
  }
}

```

--------------------------------

### Run the Sample Labeling tool Docker container

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/label-tool

Use the docker run command to start the container, mapping port 3000 to port 80 and accepting the EULA. After running, access the tool at http://localhost:3000.

```console
 docker run -it -p 3000:80 mcr.microsoft.com/azure-cognitive-services/custom-form/labeltool:latest-2.1 eula=accept

```

--------------------------------

### Output for getting a specific model (truncated)

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/how-to-guides/use-sdk-rest-api

Example output showing model details and recognized fields. The output is truncated for readability.

```text
Custom Model Info:
    Model Id: 150828c4-2eb2-487e-a728-60d5d504bd16
    Model Status: Ready
    Training model started on: 8/24/2020 7:33:25 PM +00:00
    Training model completed on: 8/24/2020 7:33:27 PM +00:00
Submodel Form Type: form-150828c4-2eb2-487e-a728-60d5d504bd16
    FieldName: CompanyAddress
    FieldName: CompanyName
    FieldName: CompanyPhoneNumber
    FieldName: DatedAs
    FieldName: Email
    FieldName: Merchant
    FieldName: PhoneNumber
    FieldName: PurchaseOrderNumber
    FieldName: Quantity
    FieldName: Signature
    FieldName: Subtotal
    FieldName: Tax
    FieldName: Total
    FieldName: VendorName
    FieldName: Website
...

```

--------------------------------

### Get all usage records endpoint

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/containers/disconnected

Returns a report summarizing all usage collected in the mounted billing record directory. The example HTTPS endpoint is http://localhost:5000/records/usage-logs.

```http
https://<service>/records/usage-logs/

```

--------------------------------

### Set up endpoint and key credentials

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/how-to-guides/use-sdk-rest-api

Create variables for your resource's key and endpoint. Replace the placeholders with your actual values. Remember to remove the key from your code when done and use secure storage for production.

```csharp
private static readonly string endpoint = "PASTE_YOUR_FORM_RECOGNIZER_ENDPOINT_HERE";
private static readonly string apiKey = "PASTE_YOUR_FORM_RECOGNIZER_SUBSCRIPTION_KEY_HERE";
private static readonly AzureKeyCredential credential = new AzureKeyCredential(apiKey);

```

--------------------------------

### Build and run the application with Gradle

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/quickstarts/get-started-sdks-rest-api

After adding the code sample, navigate to the main project directory (form-recognize-app) and run these commands to build and run the application.

```Console
gradle build
```

```Console
gradle run
```

--------------------------------

### Get model API response

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/how-to-guides/disaster-recovery

Example HTTP response body showing model information including the status field. The status indicates whether the model is ready.

```HTTP
HTTP/1.1 200 OK
Content-Type: application/json; charset=utf-8
{"modelInfo":{"modelId":"33f4d42c-cd2f-4e74-b990-a1aeafab5a5d","status":"ready","createdDateTime":"2020-02-26T16:59:28Z","lastUpdatedDateTime":"2020-02-26T16:59:34Z"},"trainResult":{"trainingDocuments":[{"documentName":"0.pdf","pages":1,"errors":[],"status":"succeeded"},{"documentName":"1.pdf","pages":1,"errors":[],"status":"succeeded"},{"documentName":"2.pdf","pages":1,"errors":[],"status":"succeeded"},{"documentName":"3.pdf","pages":1,"errors":[],"status":"succeeded"},{"documentName":"4.pdf","pages":1,"errors":[],"status":"succeeded"}],"errors":[]}}

```

--------------------------------

### Method signature for managing models (Java)

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/how-to-guides/use-sdk-rest-api

Starts the model management workflow. Copy this method signature to begin the example that performs all model management tasks in a single method.

```Java
private static void ManageModels(FormTrainingClient trainingClient, String trainingFileUrl) {

```

--------------------------------

### Complete JavaScript application setup

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/quickstarts/get-started-sdks-rest-api

Shows the complete set of lines required in the JavaScript application after following the setup steps. Includes the import statement and the endpoint and key variables.

```javascript

const { FormRecognizerClient, AzureKeyCredential } = require("@azure/ai-form-recognizer");

const endpoint = "PASTE_YOUR_FORM_RECOGNIZER_ENDPOINT_HERE";
const key = "PASTE_YOUR_FORM_RECOGNIZER_KEY_HERE";

```

--------------------------------

### Analyze PDF form with beginRecognizeCustomFormsFromUrl

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/how-to-guides/use-sdk-rest-api

Starts the recognition of a custom form from a URL using the beginRecognizeCustomFormsFromUrl method. Requires a trained model ID. The returned SyncPoller is used to get the final list of RecognizedForm objects.

```java
// Analyze PDF form data
private static void AnalyzePdfForm(FormRecognizerClient formClient, String modelId, String pdfFormUrl) {
    SyncPoller<FormRecognizerOperationResult, List<RecognizedForm>> recognizeFormPoller = formClient
            .beginRecognizeCustomFormsFromUrl(modelId, pdfFormUrl);

    List<RecognizedForm> recognizedForms = recognizeFormPoller.getFinalResult();

```

--------------------------------

### Initialize the npm project

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/quickstarts/get-started-sdks-rest-api

Runs npm init to scaffold the project. Follow the terminal prompts to set attributes like name, version, and entry point.

```bash
npm init

```

--------------------------------

### Run the Document Intelligence quickstart application

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/quickstarts/get-started-sdks-rest-api

This command runs the Python quickstart script from the terminal. It is used after adding the code sample to the application and navigating to the folder containing the form_recognizer_quickstart.py file.

```bash
python form_recognizer_quickstart.py
```

--------------------------------

### Analyze business card from URL using beginRecognizeBusinessCardsFromUrl

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/how-to-guides/use-sdk-rest-api

Starts business card analysis from a URL using the FormRecognizerClient. The method returns a SyncPoller that is used to get the final result as a list of RecognizedForm objects, one per card.

```Java
private static void AnalyzeBusinessCard(FormRecognizerClient recognizerClient, String bcUrl) {
    SyncPoller < FormRecognizerOperationResult,
    List < RecognizedForm >> recognizeBusinessCardPoller = client.beginRecognizeBusinessCardsFromUrl(businessCardUrl);

    List < RecognizedForm > businessCardPageResults = recognizeBusinessCardPoller.getFinalResult();

```

--------------------------------

### Analyze receipt from URI using beginRecognizeReceiptsFromUrl

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/how-to-guides/use-sdk-rest-api

Starts receipt analysis with a synchronous poller. The method returns a SyncPoller that must be polled to get the final list of RecognizedForm objects. Use this when you have a receipt image accessible via a URI.

```java
private static void AnalyzeReceipt(FormRecognizerClient recognizerClient, String receiptUri) {
    SyncPoller<FormRecognizerOperationResult, List<RecognizedForm>> syncPoller = recognizerClient
            .beginRecognizeReceiptsFromUrl(receiptUri);
    List<RecognizedForm> receiptPageResults = syncPoller.getFinalResult();

```

--------------------------------

### Example file list car-maint.jsonl

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/train/custom-classifier

Example JSON Lines file list for the car-maint class. Each line specifies a file path to be used for training.

```json
{"file":"classifier/car-maint/Commercial Motor Vehicle - Adatum.pdf"}
{"file":"classifier/car-maint/Commercial Motor Vehicle - Fincher.pdf"}
{"file":"classifier/car-maint/Commercial Motor Vehicle - Lamna.pdf"}
{"file":"classifier/car-maint/Commercial Motor Vehicle - Liberty.pdf"}
{"file":"classifier/car-maint/Commercial Motor Vehicle - Trey.pdf"}
```

--------------------------------

### Generate copy authorization request

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/train/custom-classifier

HTTP POST request to get copy authorization from the target resource. Replace the endpoint and key with your target resource's values. The response contains the JSON payload needed to start the copy.

```http
POST https://myendpoint.cognitiveservices.azure.com/documentintelligence/documentClassifiers:authorizeCopy?api-version=2024-11-30
Ocp-Apim-Subscription-Key: {<your-key>}
```

--------------------------------

### Complete Python application setup with imports and variables

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/quickstarts/get-started-sdks-rest-api

The full setup code for the Python application, including imports and endpoint/key variables. This is the code that should be in your application at this point.

```python
import os
from azure.core.exceptions import ResourceNotFoundError
from azure.ai.formrecognizer import DocumentAnalysisClient
from azure.core.credentials import AzureKeyCredential

endpoint = "YOUR_FORM_RECOGNIZER_ENDPOINT"
key = "YOUR_FORM_RECOGNIZER_KEY"
```

--------------------------------

### Analyze ID document from URL using beginRecognizeIdentityDocumentsFromUrl

Source: https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/how-to-guides/use-sdk-rest-api

Starts the analysis of an ID document from a URI using the FormRecognizerClient. The method returns a SyncPoller that is used to get the final result as a list of RecognizedForm objects. The variable licenseDocumentUrl is used in the call, but its definition is not shown.

```Java
private static void AnalyzeId(FormRecognizerClient client, String idUrl) {
    SyncPoller < FormRecognizerOperationResult,
    List < RecognizedForm >> analyzeIdentityDocumentPoller = client.beginRecognizeIdentityDocumentsFromUrl(licenseDocumentUrl);

    List < RecognizedForm > identityDocumentResults = analyzeIdentityDocumentPoller.getFinalResult();

```
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