Sends an online processing request to a Form Parser processor and parses the response. Extracts and prints form fields and tables.
Explore further
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
ProcessFormDocument
{
public
static
void
processFormDocument
()
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"
;
processFormDocument
(
projectId
,
location
,
processerId
,
filePath
);
}
public
static
void
processFormDocument
(
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 text recognition output from the processor
// For a full list of Document object attributes,
// please reference this page:
// https://googleapis.dev/java/google-cloud-document-ai/latest/index.html
// Get all of the document text as one big string
String
text
=
documentResponse
.
getText
();
System
.
out
.
printf
(
"Full document text: '%s'\n"
,
removeNewlines
(
text
));
// Read the text recognition output from the processor
List<Document
.
Page
>
pages
=
documentResponse
.
getPagesList
();
System
.
out
.
printf
(
"There are %s page(s) in this document.\n"
,
pages
.
size
());
for
(
Document
.
Page
page
:
pages
)
{
System
.
out
.
printf
(
"\n\n**** Page %d ****\n"
,
page
.
getPageNumber
());
List<Document
.
Page
.
Table
>
tables
=
page
.
getTablesList
();
System
.
out
.
printf
(
"Found %d table(s):\n"
,
tables
.
size
());
for
(
Document
.
Page
.
Table
table
:
tables
)
{
printTableInfo
(
table
,
text
);
}
List<Document
.
Page
.
FormField
>
formFields
=
page
.
getFormFieldsList
();
System
.
out
.
printf
(
"Found %d form fields:\n"
,
formFields
.
size
());
for
(
Document
.
Page
.
FormField
formField
:
formFields
)
{
String
fieldName
=
getLayoutText
(
formField
.
getFieldName
().
getTextAnchor
(),
text
);
String
fieldValue
=
getLayoutText
(
formField
.
getFieldValue
().
getTextAnchor
(),
text
);
System
.
out
.
printf
(
" * '%s': '%s'\n"
,
removeNewlines
(
fieldName
),
removeNewlines
(
fieldValue
));
}
}
}
}
private
static
void
printTableInfo
(
Document
.
Page
.
Table
table
,
String
text
)
{
Document
.
Page
.
Table
.
TableRow
firstBodyRow
=
table
.
getBodyRows
(
0
);
int
columnCount
=
firstBodyRow
.
getCellsCount
();
System
.
out
.
printf
(
" Table with %d columns and %d rows:\n"
,
columnCount
,
table
.
getBodyRowsCount
());
Document
.
Page
.
Table
.
TableRow
headerRow
=
table
.
getHeaderRows
(
0
);
StringBuilder
headerRowText
=
new
StringBuilder
();
for
(
Document
.
Page
.
Table
.
TableCell
cell
:
headerRow
.
getCellsList
())
{
String
columnName
=
getLayoutText
(
cell
.
getLayout
().
getTextAnchor
(),
text
);
headerRowText
.
append
(
String
.
format
(
"%s | "
,
removeNewlines
(
columnName
)));
}
headerRowText
.
setLength
(
headerRowText
.
length
()
-
3
);
System
.
out
.
printf
(
" Collumns: %s\n"
,
headerRowText
.
toString
());
StringBuilder
firstRowText
=
new
StringBuilder
();
for
(
Document
.
Page
.
Table
.
TableCell
cell
:
firstBodyRow
.
getCellsList
())
{
String
cellText
=
getLayoutText
(
cell
.
getLayout
().
getTextAnchor
(),
text
);
firstRowText
.
append
(
String
.
format
(
"%s | "
,
removeNewlines
(
cellText
)));
}
firstRowText
.
setLength
(
firstRowText
.
length
()
-
3
);
System
.
out
.
printf
(
" First row data: %s\n"
,
firstRowText
.
toString
());
}
// Extract shards from the text field
private
static
String
getLayoutText
(
Document
.
TextAnchor
textAnchor
,
String
text
)
{
if
(
textAnchor
.
getTextSegmentsList
().
size
()
>
0
)
{
int
startIdx
=
(
int
)
textAnchor
.
getTextSegments
(
0
).
getStartIndex
();
int
endIdx
=
(
int
)
textAnchor
.
getTextSegments
(
0
).
getEndIndex
();
return
text
.
substring
(
startIdx
,
endIdx
);
}
return
"[NO TEXT]"
;
}
private
static
String
removeNewlines
(
String
s
)
{
return
s
.
replace
(
"\n"
,
""
).
replace
(
"\r"
,
""
);
}
}
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 the table and form fields output from the processor
// The form processor also contains OCR data. For more information
// on how to parse OCR data please see the OCR sample.
// For a full list of Document object attributes,
// please reference this page: https://googleapis.dev/nodejs/documentai/latest/index.html
const
{
document
}
=
result
;
const
{
text
}
=
document
;
console
.
log
(
`Full document text:
${
JSON
.
stringify
(
text
)
}
`
);
console
.
log
(
`There are
${
document
.
pages
.
length
}
page(s) in this document.`
);
for
(
const
page
of
document
.
pages
)
{
console
.
log
(
`\n\n**** Page
${
page
.
pageNumber
}
****`
);
console
.
log
(
`Found
${
page
.
tables
.
length
}
table(s):`
);
for
(
const
table
of
page
.
tables
)
{
const
numCollumns
=
table
.
headerRows
[
0
].
cells
.
length
;
const
numRows
=
table
.
bodyRows
.
length
;
console
.
log
(
`Table with
${
numCollumns
}
columns and
${
numRows
}
rows:`
);
printTableInfo
(
table
,
text
);
}
console
.
log
(
`Found
${
page
.
formFields
.
length
}
form field(s):`
);
for
(
const
field
of
page
.
formFields
)
{
const
fieldName
=
getText
(
field
.
fieldName
.
textAnchor
,
text
);
const
fieldValue
=
getText
(
field
.
fieldValue
.
textAnchor
,
text
);
console
.
log
(
`\t*
${
JSON
.
stringify
(
fieldName
)
}
:
${
JSON
.
stringify
(
fieldValue
)
}
`
);
}
}
}
const
printTableInfo
=
(
table
,
text
)
=
>
{
// Print header row
let
headerRowText
=
''
;
for
(
const
headerCell
of
table
.
headerRows
[
0
].
cells
)
{
const
headerCellText
=
getText
(
headerCell
.
layout
.
textAnchor
,
text
);
headerRowText
+=
`
${
JSON
.
stringify
(
headerCellText
.
trim
())
}
| `
;
}
console
.
log
(
`Collumns:
${
headerRowText
.
substring
(
0
,
headerRowText
.
length
-
3
)
}
`
);
// Print first body row
let
bodyRowText
=
''
;
for
(
const
bodyCell
of
table
.
bodyRows
[
0
].
cells
)
{
const
bodyCellText
=
getText
(
bodyCell
.
layout
.
textAnchor
,
text
);
bodyRowText
+=
`
${
JSON
.
stringify
(
bodyCellText
.
trim
())
}
| `
;
}
console
.
log
(
`First row data:
${
bodyRowText
.
substring
(
0
,
bodyRowText
.
length
-
3
)
}
`
);
};
// Extract shards from the text field
const
getText
=
(
textAnchor
,
text
)
=
>
{
if
(
!
textAnchor
.
textSegments
||
textAnchor
.
textSegments
.
length
===
0
)
{
return
''
;
}
// First shard in document doesn't have startIndex property
const
startIndex
=
textAnchor
.
textSegments
[
0
].
startIndex
||
0
;
const
endIndex
=
textAnchor
.
textSegments
[
0
].
endIndex
;
return
text
.
substring
(
startIndex
,
endIndex
);
};
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_form_sample
(
project_id
:
str
,
location
:
str
,
processor_id
:
str
,
processor_version
:
str
,
file_path
:
str
,
mime_type
:
str
,
)
-
> documentai
.
Document
:
# Online processing request to Document AI
document
=
process_document
(
project_id
,
location
,
processor_id
,
processor_version
,
file_path
,
mime_type
)
# Read the table and form fields output from the processor
# The form processor also contains OCR data. For more information
# on how to parse OCR data please see the OCR sample.
text
=
document
.
text
print
(
f
"Full document text:
{
repr
(
text
)
}
\n
"
)
print
(
f
"There are
{
len
(
document
.
pages
)
}
page(s) in this document."
)
# Read the form fields and tables output from the processor
for
page
in
document
.
pages
:
print
(
f
"
\n\n
**** Page
{
page
.
page_number
}
****"
)
print
(
f
"
\n
Found
{
len
(
page
.
tables
)
}
table(s):"
)
for
table
in
page
.
tables
:
num_columns
=
len
(
table
.
header_rows
[
0
]
.
cells
)
num_rows
=
len
(
table
.
body_rows
)
print
(
f
"Table with
{
num_columns
}
columns and
{
num_rows
}
rows:"
)
# Print header rows
print
(
"Columns:"
)
print_table_rows
(
table
.
header_rows
,
text
)
# Print body rows
print
(
"Table body data:"
)
print_table_rows
(
table
.
body_rows
,
text
)
print
(
f
"
\n
Found
{
len
(
page
.
form_fields
)
}
form field(s):"
)
for
field
in
page
.
form_fields
:
name
=
layout_to_text
(
field
.
field_name
,
text
)
value
=
layout_to_text
(
field
.
field_value
,
text
)
print
(
f
" *
{
repr
(
name
.
strip
())
}
:
{
repr
(
value
.
strip
())
}
"
)
# Supported in version `pretrained-form-parser-v2.0-2022-11-10` and later.
# For more information: https://cloud.google.com/document-ai/docs/form-parser
if
document
.
entities
:
print
(
f
"Found
{
len
(
document
.
entities
)
}
generic entities:"
)
for
entity
in
document
.
entities
:
print_entity
(
entity
)
# Print Nested Entities
for
prop
in
entity
.
properties
:
print_entity
(
prop
)
return
document
def
print_table_rows
(
table_rows
:
Sequence
[
documentai
.
Document
.
Page
.
Table
.
TableRow
],
text
:
str
)
-
> None
:
for
table_row
in
table_rows
:
row_text
=
""
for
cell
in
table_row
.
cells
:
cell_text
=
layout_to_text
(
cell
.
layout
,
text
)
row_text
+=
f
"
{
repr
(
cell_text
.
strip
())
}
| "
print
(
row_text
)
def
print_entity
(
entity
:
documentai
.
Document
.
Entity
)
-
> None
:
# Fields detected. For a full list of fields for each processor see
# the processor documentation:
# https://cloud.google.com/document-ai/docs/processors-list
key
=
entity
.
type_
# Some other value formats in addition to text are available
# e.g. dates: `entity.normalized_value.date_value.year`
text_value
=
entity
.
text_anchor
.
content
or
entity
.
mention_text
confidence
=
entity
.
confidence
normalized_value
=
entity
.
normalized_value
.
text
print
(
f
" *
{
repr
(
key
)
}
:
{
repr
(
text_value
)
}
(
{
confidence
:
.1%
}
confident)"
)
if
normalized_value
:
print
(
f
" * Normalized Value:
{
repr
(
normalized_value
)
}
"
)
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
def
layout_to_text
(
layout
:
documentai
.
Document
.
Page
.
Layout
,
text
:
str
)
-
> str
:
"""
Document AI identifies text in different parts of the document by their
offsets in the entirety of the document"s text. This function converts
offsets to a string.
"""
# If a text segment spans several lines, it will
# be stored in different text segments.
return
""
.
join
(
text
[
int
(
segment
.
start_index
)
:
int
(
segment
.
end_index
)]
for
segment
in
layout
.
text_anchor
.
text_segments
)
What's next
To search and filter code samples for other Google Cloud products, see the Google Cloud sample browser .
