Use Machine Learning to Extract Information from Documents

You will use Swagger UI, via any web browser, to call the Document Information Extraction APIs. Swagger UI allows developers to effortlessly interact and try out every single operation an API exposes for easy consumption. For more information, see Swagger UI.

In the service key you created for Document Information Extraction in the previous tutorial: Create Service Instance for Document Information Extraction, you should find (outside the uaa section of the service key) an entry called url (as highlighted in the image below).

1. To access the Document Information Extraction Swagger UI, add /document-information-extraction/v1 to the url value, paste it in any web browser and press Enter.

   

2. To be able to use the Swagger UI endpoints you need to authorize yourself. In the top right corner, click Authorize.

   

3. Get the access_token value created in the previous tutorial: Get OAuth Access Token for Document Information Extraction Using Any Web Browser, then add bearer in front of it, and enter in the Value field.

   bearer <access_token>
   

   

4. Click Authorize, and then click Close.

   

Use the GET /capabilities endpoint to see a list of document fields you can extract with Document Information Extraction.

Click the endpoint name to expand it, click Try it out, and then Execute.

You should receive a response like the following:

If you get an error response code 401 (Unauthorized), your token is probably incorrect. Check if you have added the word bearer before the token and if the token value is complete and has been properly copied from the access_token value you received in the previous tutorial: Get OAuth Access Token for Document Information Extraction via Web Browser.

To use Document Information Extraction, you need to create a client. This client is used in most of the endpoints to distinguish and separate data.

You can either create only one client or create a list of clients in the payload field of the POST /clients endpoint. The clientId values created here can be used in all the other service endpoints.

1. Expand the POST /clients endpoint.

2. Click Try it out.

3. Enter your clientId and clientName values in the payload field in the format you see in Examples for payload parameter.

4. Click Execute.

You should receive a response like the following:

CAUTION:

Be aware of the following Document Information Extraction trial account limitations:​

• Maximum 40 uploaded documents per week​

• Maximum 10 created clientIds

• Maximum 10 created enrichment dataIds

To see a list of the clients you have created:

1. Expand the GET /clients endpoint.

2. Click Try it out.

3. Enter a maximum number of clients to be listed in the limit field.

4. Click Execute.

You should receive a response like the following:

Document Information Extraction uses a globally pre-trained machine learning model that currently obtains better accuracy results with invoices and payment advices in English. The team is working to support additional document types and languages in the near future.

Upload to the service any document in PDF format that has content in headers and tables, such as an invoice, using the POST /document/jobs endpoint. You need to specify the following:

• A file.

• A clientId.

• The fields to be extracted. You can find the list of available fields that can be extracted in the response of the GET /capabilities endpoint.

Do the following:

1. Expand the POST /document/jobs endpoint.

2. Click Try it out.

3. Upload a document PDF file.

4. Enter the list of fields to be extracted from the uploaded file (documentNumber,taxId,purchaseOrder,shippingAmount,subTotalAmount,vendorAddress,vendorName,totalAmount,currencyCode, for example), a client id or a list of client ids (c_00, for example), and the document type (invoice, for example).

5. Click Execute.

   

After you have clicked Execute, you should receive a response like the following:

Copy the id from the Response body to see the result of the extraction in the GET /document/jobs/{uuid} endpoint.

You can now use the GET /document/jobs/{uuid} endpoint to receive the prediction.

1. Expand the endpoint.

2. Click Try it out.

3. Enter the clientId you used in the previous step (c_00, for example).

4. Enter the id received in the POST /jobs endpoint as the uuid.

5. Click Execute.

   

You should receive a response like the following:

In the response, you will find some general information about the document you uploaded. In the headerFields, such as documentType and fieldName, you will find the prediction for the extracted fields. If the status of the document (indicated by the status field) is PENDING instead of DONE, then it means that the service is still extracting some fields and the returned JSON file does not yet contain all the requested fields.

Congratulations, you completed the tutorial. You have now used our machine learning model to get field value predictions for the document you uploaded to Document Information Extraction.