Sends an online processing request to a Specialized Splitter/Classifier processor and parses the response. Extracts and prints document classifications and page ranges.
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For detailed documentation that includes this code sample, see the following:
Code sample
Java
For more information, see the Document AI Java API reference documentation .
To authenticate to Document AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .
import
com.google.cloud.documentai.v1beta3.Document
;
import
com.google.cloud.documentai.v1beta3.DocumentProcessorServiceClient
;
import
com.google.cloud.documentai.v1beta3.DocumentProcessorServiceSettings
;
import
com.google.cloud.documentai.v1beta3.ProcessRequest
;
import
com.google.cloud.documentai.v1beta3.ProcessResponse
;
import
com.google.cloud.documentai.v1beta3.RawDocument
;
import
com.google.protobuf. ByteString
;
import
java.io.IOException
;
import
java.nio.file.Files
;
import
java.nio.file.Paths
;
import
java.util.List
;
import
java.util.concurrent.ExecutionException
;
import
java.util.concurrent.TimeoutException
;
public
class
ProcessSplitterDocument
{
public
static
void
processSplitterDocument
()
throws
IOException
,
InterruptedException
,
ExecutionException
,
TimeoutException
{
// TODO(developer): Replace these variables before running the sample.
String
projectId
=
"your-project-id"
;
String
location
=
"your-project-location"
;
// Format is "us" or "eu".
String
processerId
=
"your-processor-id"
;
String
filePath
=
"path/to/input/file.pdf"
;
processSplitterDocument
(
projectId
,
location
,
processerId
,
filePath
);
}
public
static
void
processSplitterDocument
(
String
projectId
,
String
location
,
String
processorId
,
String
filePath
)
throws
IOException
,
InterruptedException
,
ExecutionException
,
TimeoutException
{
// Initialize client that will be used to send requests. This client only needs
// to be created
// once, and can be reused for multiple requests. After completing all of your
// requests, call
// the "close" method on the client to safely clean up any remaining background
// resources.
String
endpoint
=
String
.
format
(
"%s-documentai.googleapis.com:443"
,
location
);
DocumentProcessorServiceSettings
settings
=
DocumentProcessorServiceSettings
.
newBuilder
().
setEndpoint
(
endpoint
).
build
();
try
(
DocumentProcessorServiceClient
client
=
DocumentProcessorServiceClient
.
create
(
settings
))
{
// The full resource name of the processor, e.g.:
// projects/project-id/locations/location/processor/processor-id
// You must create new processors in the Cloud Console first
String
name
=
String
.
format
(
"projects/%s/locations/%s/processors/%s"
,
projectId
,
location
,
processorId
);
// Read the file.
byte
[]
imageFileData
=
Files
.
readAllBytes
(
Paths
.
get
(
filePath
));
// Convert the image data to a Buffer and base64 encode it.
ByteString
content
=
ByteString
.
copyFrom
(
imageFileData
);
RawDocument
document
=
RawDocument
.
newBuilder
().
setContent
(
content
).
setMimeType
(
"application/pdf"
).
build
();
// Configure the process request.
ProcessRequest
request
=
ProcessRequest
.
newBuilder
().
setName
(
name
).
setRawDocument
(
document
).
build
();
// Recognizes text entities in the PDF document
ProcessResponse
result
=
client
.
processDocument
(
request
);
Document
documentResponse
=
result
.
getDocument
();
System
.
out
.
println
(
"Document processing complete."
);
// Read the splitter output from the document splitter processor:
// https://cloud.google.com/document-ai/docs/processors-list#processor_doc-splitter
// This processor only provides text for the document and information on how
// to split the document on logical boundaries. To identify and extract text,
// form elements, and entities please see other processors like the OCR, form,
// and specalized processors.
List<Document
.
Entity
>
entities
=
documentResponse
.
getEntitiesList
();
System
.
out
.
printf
(
"Found %d subdocuments:\n"
,
entities
.
size
());
for
(
Document
.
Entity
entity
:
entities
)
{
float
entityConfidence
=
entity
.
getConfidence
();
String
pagesRangeText
=
pageRefsToString
(
entity
.
getPageAnchor
().
getPageRefsList
());
String
subdocumentType
=
entity
.
getType
();
if
(
subdocumentType
.
isEmpty
())
{
System
.
out
.
printf
(
"%.2f%% confident that %s a subdocument.\n"
,
entityConfidence
*
100
,
pagesRangeText
);
}
else
{
System
.
out
.
printf
(
"%.2f%% confident that %s a '%s' subdocument.\n"
,
entityConfidence
*
100
,
pagesRangeText
,
subdocumentType
);
}
}
}
}
// Converts page reference(s) to a string describing the page or page range.
private
static
String
pageRefsToString
(
List<Document
.
PageAnchor
.
PageRef
>
pageRefs
)
{
if
(
pageRefs
.
size
()
==
1
)
{
return
String
.
format
(
"page %d is"
,
pageRefs
.
get
(
0
).
getPage
()
+
1
);
}
else
{
long
start
=
pageRefs
.
get
(
0
).
getPage
()
+
1
;
long
end
=
pageRefs
.
get
(
1
).
getPage
()
+
1
;
return
String
.
format
(
"pages %d to %d are"
,
start
,
end
);
}
}
}
Node.js
For more information, see the Document AI Node.js API reference documentation .
To authenticate to Document AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION'; // Format is 'us' or 'eu'
// const processorId = 'YOUR_PROCESSOR_ID'; // Create processor in Cloud Console
// const filePath = '/path/to/local/pdf';
const
{
DocumentProcessorServiceClient
}
=
require
(
' @google-cloud/documentai
'
).
v1beta3
;
// Instantiates a client
const
client
=
new
DocumentProcessorServiceClient
();
async
function
processDocument
()
{
// The full resource name of the processor, e.g.:
// projects/project-id/locations/location/processor/processor-id
// You must create new processors in the Cloud Console first
const
name
=
`projects/
${
projectId
}
/locations/
${
location
}
/processors/
${
processorId
}
`
;
// Read the file into memory.
const
fs
=
require
(
'fs'
).
promises
;
const
imageFile
=
await
fs
.
readFile
(
filePath
);
// Convert the image data to a Buffer and base64 encode it.
const
encodedImage
=
Buffer
.
from
(
imageFile
).
toString
(
'base64'
);
const
request
=
{
name
,
rawDocument
:
{
content
:
encodedImage
,
mimeType
:
'application/pdf'
,
},
};
// Recognizes text entities in the PDF document
const
[
result
]
=
await
client
.
processDocument
(
request
);
console
.
log
(
'Document processing complete.'
);
// Read fields specificly from the specalized US drivers license processor:
// https://cloud.google.com/document-ai/docs/processors-list#processor_us-driver-license-parser
// retriving data from other specalized processors follow a similar pattern.
// For a complete list of processors see:
// https://cloud.google.com/document-ai/docs/processors-list
//
// OCR and other data is also present in the quality processor's response.
// Please see the OCR and other samples for how to parse other data in the
// response.
const
{
document
}
=
result
;
console
.
log
(
`Found
${
document
.
entities
.
length
}
subdocuments:`
);
for
(
const
entity
of
document
.
entities
)
{
const
conf
=
entity
.
confidence
*
100
;
const
pagesRange
=
pageRefsToRange
(
entity
.
pageAnchor
.
pageRefs
);
if
(
entity
.
type
!==
''
)
{
console
.
log
(
`
${
conf
.
toFixed
(
2
)
}
% confident that
${
pagesRange
}
a "
${
entity
.
type
}
" subdocument.`
);
}
else
{
console
.
log
(
`
${
conf
.
toFixed
(
2
)
}
% confident that
${
pagesRange
}
a subdocument.`
);
}
}
}
// Converts a page ref to a string describing the page or page range.
const
pageRefsToRange
=
pageRefs
=
>
{
if
(
pageRefs
.
length
===
1
)
{
const
num
=
parseInt
(
pageRefs
[
0
].
page
)
+
1
||
1
;
return
`page
${
num
}
is`
;
}
else
{
const
start
=
parseInt
(
pageRefs
[
0
].
page
)
+
1
||
1
;
const
end
=
parseInt
(
pageRefs
[
1
].
page
)
+
1
;
return
`pages
${
start
}
to
${
end
}
are`
;
}
};
Python
For more information, see the Document AI Python API reference documentation .
To authenticate to Document AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .
from
typing
import
Optional
,
Sequence
from
google.api_core.client_options
import
ClientOptions
from
google.cloud
import
documentai
# TODO(developer): Uncomment these variables before running the sample.
# project_id = "YOUR_PROJECT_ID"
# location = "YOUR_PROCESSOR_LOCATION" # Format is "us" or "eu"
# processor_id = "YOUR_PROCESSOR_ID" # Create processor before running sample
# processor_version = "rc" # Refer to https://cloud.google.com/document-ai/docs/manage-processor-versions for more information
# file_path = "/path/to/local/pdf"
# mime_type = "application/pdf" # Refer to https://cloud.google.com/document-ai/docs/file-types for supported file types
def
process_document_splitter_sample
(
project_id
:
str
,
location
:
str
,
processor_id
:
str
,
processor_version
:
str
,
file_path
:
str
,
mime_type
:
str
,
)
-
> None
:
# Online processing request to Document AI
document
=
process_document
(
project_id
,
location
,
processor_id
,
processor_version
,
file_path
,
mime_type
)
# Read the splitter output from a document splitter/classifier processor:
# e.g. https://cloud.google.com/document-ai/docs/processors-list#processor_procurement-document-splitter
# This processor only provides text for the document and information on how
# to split the document on logical boundaries. To identify and extract text,
# form elements, and entities please see other processors like the OCR, form,
# and specalized processors.
print
(
f
"Found
{
len
(
document
.
entities
)
}
subdocuments:"
)
for
entity
in
document
.
entities
:
conf_percent
=
f
"
{
entity
.
confidence
:
.1%
}
"
pages_range
=
page_refs_to_string
(
entity
.
page_anchor
.
page_refs
)
# Print subdocument type information, if available
if
entity
.
type_
:
print
(
f
"
{
conf_percent
}
confident that
{
pages_range
}
a '
{
entity
.
type_
}
' subdocument."
)
else
:
print
(
f
"
{
conf_percent
}
confident that
{
pages_range
}
a subdocument."
)
def
page_refs_to_string
(
page_refs
:
Sequence
[
documentai
.
Document
.
PageAnchor
.
PageRef
],
)
-
> str
:
"""Converts a page ref to a string describing the page or page range."""
pages
=
[
str
(
int
(
page_ref
.
page
)
+
1
)
for
page_ref
in
page_refs
]
if
len
(
pages
)
==
1
:
return
f
"page
{
pages
[
0
]
}
is"
else
:
return
f
"pages
{
', '
.
join
(
pages
)
}
are"
def
process_document
(
project_id
:
str
,
location
:
str
,
processor_id
:
str
,
processor_version
:
str
,
file_path
:
str
,
mime_type
:
str
,
process_options
:
Optional
[
documentai
.
ProcessOptions
]
=
None
,
)
-
> documentai
.
Document
:
# You must set the `api_endpoint` if you use a location other than "us".
client
=
documentai
.
DocumentProcessorServiceClient
(
client_options
=
ClientOptions
(
api_endpoint
=
f
"
{
location
}
-documentai.googleapis.com"
)
)
# The full resource name of the processor version, e.g.:
# `projects/{project_id}/locations/{location}/processors/{processor_id}/processorVersions/{processor_version_id}`
# You must create a processor before running this sample.
name
=
client
.
processor_version_path
(
project_id
,
location
,
processor_id
,
processor_version
)
# Read the file into memory
with
open
(
file_path
,
"rb"
)
as
image
:
image_content
=
image
.
read
()
# Configure the process request
request
=
documentai
.
ProcessRequest
(
name
=
name
,
raw_document
=
documentai
.
RawDocument
(
content
=
image_content
,
mime_type
=
mime_type
),
# Only supported for Document OCR processor
process_options
=
process_options
,
)
result
=
client
.
process_document
(
request
=
request
)
# For a full list of `Document` object attributes, reference this page:
# https://cloud.google.com/document-ai/docs/reference/rest/v1/Document
return
result
.
document
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