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add prebuilt document to readme examples + print styles (#20996)
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‎sdk/formrecognizer/azure-ai-formrecognizer/README.md‎

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@@ -191,6 +191,7 @@ The following section provides several code snippets covering some of the most c
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* [Extract layout](#extract-layout "Extract Layout")
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* [Using Prebuilt Models](#using-prebuilt-models "Using Prebuilt Models")
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* [Using Prebuilt Document](#using-prebuilt-document "Using Prebuilt Document")
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* [Build a Model](#build-a-model "Build a model")
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* [Analyze Documents Using a Custom Model](#analyze-documents-using-a-custom-model "Analyze Documents Using a Custom Model")
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* [Manage Your Models](#manage-your-models "Manage Your Models")
@@ -310,6 +311,83 @@ You are not limited to receipts! There are a few prebuilt models to choose from,
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- Analyze invoices using the `prebuilt-invoice` model (fields recognized by the service can be found [here][service_recognize_invoice]).
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- Analyze identity documents using the `prebuilt-idDocuments` model (fields recognized by the service can be found [here][service_recognize_identity_documents]).
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### Using Prebuilt Document
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Analyze entities, key-value pairs, tables, styles, and selection marks from documents using the general prebuilt document model provided by the Form Recognizer service.
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Select the Prebuilt Document model by passing `model="prebuilt-document"` into the `begin_analyze_documents` method:
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```python
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from azure.ai.formrecognizer import DocumentAnalysisClient
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from azure.core.credentials import AzureKeyCredential
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endpoint = "https://<my-custom-subdomain>.cognitiveservices.azure.com/"
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credential = AzureKeyCredential("<api_key>")
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document_analysis_client = DocumentAnalysisClient(endpoint, credential)
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with open("<path to your document>", "rb") as fd:
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document = fd.read()
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poller = document_analysis_client.begin_analyze_document("prebuilt-document", document)
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result = poller.result()
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print("----Entities found in document----")
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for entity in result.entities:
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print("Entity '{}' has category '{}' with sub-category '{}'".format(
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entity.content, entity.category, entity.sub_category
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))
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print("...with confidence {}\n".format(entity.confidence))
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print("----Key-value pairs found in document----")
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for kv_pair in result.key_value_pairs:
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if kv_pair.key:
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print(
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"Key '{}' found within '{}' bounding regions".format(
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kv_pair.key.content,
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kv_pair.key.bounding_regions,
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)
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)
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if kv_pair.value:
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print(
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"Value '{}' found within '{}' bounding regions\n".format(
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kv_pair.value.content,
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kv_pair.value.bounding_regions,
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)
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)
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print("----Tables found in document----")
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for table_idx, table in enumerate(result.tables):
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print(
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"Table # {} has {} rows and {} columns".format(
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table_idx, table.row_count, table.column_count
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)
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)
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for region in table.bounding_regions:
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print(
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"Table # {} location on page: {} is {}".format(
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table_idx,
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region.page_number,
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region.bounding_box,
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)
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)
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print("----Styles found in document----")
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for style in result.styles:
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if style.is_handwritten:
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print("Document contains handwritten content: ")
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print(",".join([result.content[span.offset:span.offset + span.length] for span in style.spans]))
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print("----Selection marks found in document----")
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for page in result.pages:
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for selection_mark in page.selection_marks:
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print(
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"...Selection mark is '{}' within bounding box '{}' and has a confidence of {}".format(
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selection_mark.state,
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selection_mark.bounding_box,
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selection_mark.confidence,
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)
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)
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```
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### Build a model
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Build a custom model on your own document type. The resulting model can be used to analyze values from the types of documents it was trained on.
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Provide a container SAS URL to your Azure Storage Blob container where you're storing the training documents.

‎sdk/formrecognizer/azure-ai-formrecognizer/samples/v3.2-beta/async_samples/sample_analyze_prebuilt_document_async.py‎

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@@ -63,12 +63,10 @@ async def analyze_document():
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)
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result = await poller.result()
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for idx, style in enumerate(result.styles):
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print(
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"Document contains {} content".format(
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"handwritten" if style.is_handwritten else "no handwritten"
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)
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)
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for style in result.styles:
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if style.is_handwritten:
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print("Document contains handwritten content: ")
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print(",".join([result.content[span.offset:span.offset + span.length] for span in style.spans]))
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for idx, page in enumerate(result.pages):
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print("----Analyzing document from page #{}----".format(idx + 1))

‎sdk/formrecognizer/azure-ai-formrecognizer/samples/v3.2-beta/sample_analyze_prebuilt_document.py‎

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@@ -59,12 +59,10 @@ def analyze_document():
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)
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result = poller.result()
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for idx, style in enumerate(result.styles):
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print(
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"Document contains {} content".format(
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"handwritten" if style.is_handwritten else "no handwritten"
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)
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)
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for style in result.styles:
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if style.is_handwritten:
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print("Document contains handwritten content: ")
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print(",".join([result.content[span.offset:span.offset + span.length] for span in style.spans]))
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for page in result.pages:
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print("----Analyzing document from page #{}----".format(page.page_number))

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