Sends an online processing request to a Document OCR processor and parses the response. Extracts and prints full text, page dimensions, detected languages, paragraphs, blocks, lines, and tokens.
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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
ProcessOcrDocument
{
public
static
void
processOcrDocument
()
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"
;
processOcrDocument
(
projectId
,
location
,
processerId
,
filePath
);
}
public
static
void
processOcrDocument
(
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"
,
escapeNewlines
(
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
(
"Page %d:\n"
,
page
.
getPageNumber
());
printPageDimensions
(
page
.
getDimension
());
printDetectedLanguages
(
page
.
getDetectedLanguagesList
());
printParagraphs
(
page
.
getParagraphsList
(),
text
);
printBlocks
(
page
.
getBlocksList
(),
text
);
printLines
(
page
.
getLinesList
(),
text
);
printTokens
(
page
.
getTokensList
(),
text
);
}
}
}
private
static
void
printPageDimensions
(
Document
.
Page
.
Dimension
dimension
)
{
String
unit
=
dimension
.
getUnit
();
System
.
out
.
printf
(
" Width: %.1f %s\n"
,
dimension
.
getWidth
(),
unit
);
System
.
out
.
printf
(
" Height: %.1f %s\n"
,
dimension
.
getHeight
(),
unit
);
}
private
static
void
printDetectedLanguages
(
List<Document
.
Page
.
DetectedLanguage
>
detectedLangauges
)
{
System
.
out
.
println
(
" Detected languages:"
);
for
(
Document
.
Page
.
DetectedLanguage
detectedLanguage
:
detectedLangauges
)
{
String
languageCode
=
detectedLanguage
.
getLanguageCode
();
float
confidence
=
detectedLanguage
.
getConfidence
();
System
.
out
.
printf
(
" %s (%.2f%%)\n"
,
languageCode
,
confidence
*
100.0
);
}
}
private
static
void
printParagraphs
(
List<Document
.
Page
.
Paragraph
>
paragraphs
,
String
text
)
{
System
.
out
.
printf
(
" %d paragraphs detected:\n"
,
paragraphs
.
size
());
Document
.
Page
.
Paragraph
firstParagraph
=
paragraphs
.
get
(
0
);
String
firstParagraphText
=
getLayoutText
(
firstParagraph
.
getLayout
().
getTextAnchor
(),
text
);
System
.
out
.
printf
(
" First paragraph text: %s\n"
,
escapeNewlines
(
firstParagraphText
));
Document
.
Page
.
Paragraph
lastParagraph
=
paragraphs
.
get
(
paragraphs
.
size
()
-
1
);
String
lastParagraphText
=
getLayoutText
(
lastParagraph
.
getLayout
().
getTextAnchor
(),
text
);
System
.
out
.
printf
(
" Last paragraph text: %s\n"
,
escapeNewlines
(
lastParagraphText
));
}
private
static
void
printBlocks
(
List<Document
.
Page
.
Block
>
blocks
,
String
text
)
{
System
.
out
.
printf
(
" %d blocks detected:\n"
,
blocks
.
size
());
Document
.
Page
.
Block
firstBlock
=
blocks
.
get
(
0
);
String
firstBlockText
=
getLayoutText
(
firstBlock
.
getLayout
().
getTextAnchor
(),
text
);
System
.
out
.
printf
(
" First block text: %s\n"
,
escapeNewlines
(
firstBlockText
));
Document
.
Page
.
Block
lastBlock
=
blocks
.
get
(
blocks
.
size
()
-
1
);
String
lastBlockText
=
getLayoutText
(
lastBlock
.
getLayout
().
getTextAnchor
(),
text
);
System
.
out
.
printf
(
" Last block text: %s\n"
,
escapeNewlines
(
lastBlockText
));
}
private
static
void
printLines
(
List<Document
.
Page
.
Line
>
lines
,
String
text
)
{
System
.
out
.
printf
(
" %d lines detected:\n"
,
lines
.
size
());
Document
.
Page
.
Line
firstLine
=
lines
.
get
(
0
);
String
firstLineText
=
getLayoutText
(
firstLine
.
getLayout
().
getTextAnchor
(),
text
);
System
.
out
.
printf
(
" First line text: %s\n"
,
escapeNewlines
(
firstLineText
));
Document
.
Page
.
Line
lastLine
=
lines
.
get
(
lines
.
size
()
-
1
);
String
lastLineText
=
getLayoutText
(
lastLine
.
getLayout
().
getTextAnchor
(),
text
);
System
.
out
.
printf
(
" Last line text: %s\n"
,
escapeNewlines
(
lastLineText
));
}
private
static
void
printTokens
(
List<Document
.
Page
.
Token
>
tokens
,
String
text
)
{
System
.
out
.
printf
(
" %d tokens detected:\n"
,
tokens
.
size
());
Document
.
Page
.
Token
firstToken
=
tokens
.
get
(
0
);
String
firstTokenText
=
getLayoutText
(
firstToken
.
getLayout
().
getTextAnchor
(),
text
);
System
.
out
.
printf
(
" First token text: %s\n"
,
escapeNewlines
(
firstTokenText
));
Document
.
Page
.
Token
lastToken
=
tokens
.
get
(
tokens
.
size
()
-
1
);
String
lastTokenText
=
getLayoutText
(
lastToken
.
getLayout
().
getTextAnchor
(),
text
);
System
.
out
.
printf
(
" Last token text: %s\n"
,
escapeNewlines
(
lastTokenText
));
}
// 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
escapeNewlines
(
String
s
)
{
return
s
.
replace
(
"\n"
,
"\\n"
).
replace
(
"\r"
,
"\\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 text recognition output from the processor
// 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
;
// Read the text recognition output from the processor
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
(
`Page
${
page
.
pageNumber
}
`
);
printPageDimensions
(
page
.
dimension
);
printDetectedLanguages
(
page
.
detectedLanguages
);
printParagraphs
(
page
.
paragraphs
,
text
);
printBlocks
(
page
.
blocks
,
text
);
printLines
(
page
.
lines
,
text
);
printTokens
(
page
.
tokens
,
text
);
}
}
const
printPageDimensions
=
dimension
=
>
{
console
.
log
(
` Width:
${
dimension
.
width
}
`
);
console
.
log
(
` Height:
${
dimension
.
height
}
`
);
};
const
printDetectedLanguages
=
detectedLanguages
=
>
{
console
.
log
(
' Detected languages:'
);
for
(
const
lang
of
detectedLanguages
)
{
const
code
=
lang
.
languageCode
;
const
confPercent
=
lang
.
confidence
*
100
;
console
.
log
(
`
${
code
}
(
${
confPercent
.
toFixed
(
2
)
}
% confidence)`
);
}
};
const
printParagraphs
=
(
paragraphs
,
text
)
=
>
{
console
.
log
(
`
${
paragraphs
.
length
}
paragraphs detected:`
);
const
firstParagraphText
=
getText
(
paragraphs
[
0
].
layout
.
textAnchor
,
text
);
console
.
log
(
` First paragraph text:
${
JSON
.
stringify
(
firstParagraphText
)
}
`
);
const
lastParagraphText
=
getText
(
paragraphs
[
paragraphs
.
length
-
1
].
layout
.
textAnchor
,
text
);
console
.
log
(
` Last paragraph text:
${
JSON
.
stringify
(
lastParagraphText
)
}
`
);
};
const
printBlocks
=
(
blocks
,
text
)
=
>
{
console
.
log
(
`
${
blocks
.
length
}
blocks detected:`
);
const
firstBlockText
=
getText
(
blocks
[
0
].
layout
.
textAnchor
,
text
);
console
.
log
(
` First block text:
${
JSON
.
stringify
(
firstBlockText
)
}
`
);
const
lastBlockText
=
getText
(
blocks
[
blocks
.
length
-
1
].
layout
.
textAnchor
,
text
);
console
.
log
(
` Last block text:
${
JSON
.
stringify
(
lastBlockText
)
}
`
);
};
const
printLines
=
(
lines
,
text
)
=
>
{
console
.
log
(
`
${
lines
.
length
}
lines detected:`
);
const
firstLineText
=
getText
(
lines
[
0
].
layout
.
textAnchor
,
text
);
console
.
log
(
` First line text:
${
JSON
.
stringify
(
firstLineText
)
}
`
);
const
lastLineText
=
getText
(
lines
[
lines
.
length
-
1
].
layout
.
textAnchor
,
text
);
console
.
log
(
` Last line text:
${
JSON
.
stringify
(
lastLineText
)
}
`
);
};
const
printTokens
=
(
tokens
,
text
)
=
>
{
console
.
log
(
`
${
tokens
.
length
}
tokens detected:`
);
const
firstTokenText
=
getText
(
tokens
[
0
].
layout
.
textAnchor
,
text
);
console
.
log
(
` First token text:
${
JSON
.
stringify
(
firstTokenText
)
}
`
);
const
firstTokenBreakType
=
tokens
[
0
].
detectedBreak
.
type
;
console
.
log
(
` First token break type:
${
firstTokenBreakType
}
`
);
const
lastTokenText
=
getText
(
tokens
[
tokens
.
length
-
1
].
layout
.
textAnchor
,
text
);
console
.
log
(
` Last token text:
${
JSON
.
stringify
(
lastTokenText
)
}
`
);
const
lastTokenBreakType
=
tokens
[
tokens
.
length
-
1
].
detectedBreak
.
type
;
console
.
log
(
` Last token break type:
${
lastTokenBreakType
}
`
);
};
// 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_ocr_sample
(
project_id
:
str
,
location
:
str
,
processor_id
:
str
,
processor_version
:
str
,
file_path
:
str
,
mime_type
:
str
,
)
-
> None
:
# Optional: Additional configurations for Document OCR Processor.
# For more information: https://cloud.google.com/document-ai/docs/enterprise-document-ocr
process_options
=
documentai
.
ProcessOptions
(
ocr_config
=
documentai
.
OcrConfig
(
enable_native_pdf_parsing
=
True
,
enable_image_quality_scores
=
True
,
enable_symbol
=
True
,
# OCR Add Ons https://cloud.google.com/document-ai/docs/ocr-add-ons
premium_features
=
documentai
.
OcrConfig
.
PremiumFeatures
(
compute_style_info
=
True
,
enable_math_ocr
=
False
,
# Enable to use Math OCR Model
enable_selection_mark_detection
=
True
,
),
)
)
# Online processing request to Document AI
document
=
process_document
(
project_id
,
location
,
processor_id
,
processor_version
,
file_path
,
mime_type
,
process_options
=
process_options
,
)
text
=
document
.
text
print
(
f
"Full document text:
{
text
}
\n
"
)
print
(
f
"There are
{
len
(
document
.
pages
)
}
page(s) in this document.
\n
"
)
for
page
in
document
.
pages
:
print
(
f
"Page
{
page
.
page_number
}
:"
)
print_page_dimensions
(
page
.
dimension
)
print_detected_languages
(
page
.
detected_languages
)
print_blocks
(
page
.
blocks
,
text
)
print_paragraphs
(
page
.
paragraphs
,
text
)
print_lines
(
page
.
lines
,
text
)
print_tokens
(
page
.
tokens
,
text
)
if
page
.
symbols
:
print_symbols
(
page
.
symbols
,
text
)
if
page
.
image_quality_scores
:
print_image_quality_scores
(
page
.
image_quality_scores
)
if
page
.
visual_elements
:
print_visual_elements
(
page
.
visual_elements
,
text
)
def
print_page_dimensions
(
dimension
:
documentai
.
Document
.
Page
.
Dimension
)
-
> None
:
print
(
f
" Width:
{
str
(
dimension
.
width
)
}
"
)
print
(
f
" Height:
{
str
(
dimension
.
height
)
}
"
)
def
print_detected_languages
(
detected_languages
:
Sequence
[
documentai
.
Document
.
Page
.
DetectedLanguage
],
)
-
> None
:
print
(
" Detected languages:"
)
for
lang
in
detected_languages
:
print
(
f
"
{
lang
.
language_code
}
(
{
lang
.
confidence
:
.1%
}
confidence)"
)
def
print_blocks
(
blocks
:
Sequence
[
documentai
.
Document
.
Page
.
Block
],
text
:
str
)
-
> None
:
print
(
f
"
{
len
(
blocks
)
}
blocks detected:"
)
first_block_text
=
layout_to_text
(
blocks
[
0
]
.
layout
,
text
)
print
(
f
" First text block:
{
repr
(
first_block_text
)
}
"
)
last_block_text
=
layout_to_text
(
blocks
[
-
1
]
.
layout
,
text
)
print
(
f
" Last text block:
{
repr
(
last_block_text
)
}
"
)
def
print_paragraphs
(
paragraphs
:
Sequence
[
documentai
.
Document
.
Page
.
Paragraph
],
text
:
str
)
-
> None
:
print
(
f
"
{
len
(
paragraphs
)
}
paragraphs detected:"
)
first_paragraph_text
=
layout_to_text
(
paragraphs
[
0
]
.
layout
,
text
)
print
(
f
" First paragraph text:
{
repr
(
first_paragraph_text
)
}
"
)
last_paragraph_text
=
layout_to_text
(
paragraphs
[
-
1
]
.
layout
,
text
)
print
(
f
" Last paragraph text:
{
repr
(
last_paragraph_text
)
}
"
)
def
print_lines
(
lines
:
Sequence
[
documentai
.
Document
.
Page
.
Line
],
text
:
str
)
-
> None
:
print
(
f
"
{
len
(
lines
)
}
lines detected:"
)
first_line_text
=
layout_to_text
(
lines
[
0
]
.
layout
,
text
)
print
(
f
" First line text:
{
repr
(
first_line_text
)
}
"
)
last_line_text
=
layout_to_text
(
lines
[
-
1
]
.
layout
,
text
)
print
(
f
" Last line text:
{
repr
(
last_line_text
)
}
"
)
def
print_tokens
(
tokens
:
Sequence
[
documentai
.
Document
.
Page
.
Token
],
text
:
str
)
-
> None
:
print
(
f
"
{
len
(
tokens
)
}
tokens detected:"
)
first_token_text
=
layout_to_text
(
tokens
[
0
]
.
layout
,
text
)
first_token_break_type
=
tokens
[
0
]
.
detected_break
.
type_
.
name
print
(
f
" First token text:
{
repr
(
first_token_text
)
}
"
)
print
(
f
" First token break type:
{
repr
(
first_token_break_type
)
}
"
)
if
tokens
[
0
]
.
style_info
:
print_style_info
(
tokens
[
0
]
.
style_info
)
last_token_text
=
layout_to_text
(
tokens
[
-
1
]
.
layout
,
text
)
last_token_break_type
=
tokens
[
-
1
]
.
detected_break
.
type_
.
name
print
(
f
" Last token text:
{
repr
(
last_token_text
)
}
"
)
print
(
f
" Last token break type:
{
repr
(
last_token_break_type
)
}
"
)
if
tokens
[
-
1
]
.
style_info
:
print_style_info
(
tokens
[
-
1
]
.
style_info
)
def
print_symbols
(
symbols
:
Sequence
[
documentai
.
Document
.
Page
.
Symbol
],
text
:
str
)
-
> None
:
print
(
f
"
{
len
(
symbols
)
}
symbols detected:"
)
first_symbol_text
=
layout_to_text
(
symbols
[
0
]
.
layout
,
text
)
print
(
f
" First symbol text:
{
repr
(
first_symbol_text
)
}
"
)
last_symbol_text
=
layout_to_text
(
symbols
[
-
1
]
.
layout
,
text
)
print
(
f
" Last symbol text:
{
repr
(
last_symbol_text
)
}
"
)
def
print_image_quality_scores
(
image_quality_scores
:
documentai
.
Document
.
Page
.
ImageQualityScores
,
)
-
> None
:
print
(
f
" Quality score:
{
image_quality_scores
.
quality_score
:
.1%
}
"
)
print
(
" Detected defects:"
)
for
detected_defect
in
image_quality_scores
.
detected_defects
:
print
(
f
"
{
detected_defect
.
type_
}
:
{
detected_defect
.
confidence
:
.1%
}
"
)
def
print_style_info
(
style_info
:
documentai
.
Document
.
Page
.
Token
.
StyleInfo
)
-
> None
:
"""
Only supported in version `pretrained-ocr-v2.0-2023-06-02`
"""
print
(
f
" Font Size:
{
style_info
.
font_size
}
pt"
)
print
(
f
" Font Type:
{
style_info
.
font_type
}
"
)
print
(
f
" Bold:
{
style_info
.
bold
}
"
)
print
(
f
" Italic:
{
style_info
.
italic
}
"
)
print
(
f
" Underlined:
{
style_info
.
underlined
}
"
)
print
(
f
" Handwritten:
{
style_info
.
handwritten
}
"
)
print
(
f
" Text Color (RGBa):
{
style_info
.
text_color
.
red
}
,
{
style_info
.
text_color
.
green
}
,
{
style_info
.
text_color
.
blue
}
,
{
style_info
.
text_color
.
alpha
}
"
)
def
print_visual_elements
(
visual_elements
:
Sequence
[
documentai
.
Document
.
Page
.
VisualElement
],
text
:
str
)
-
> None
:
"""
Only supported in version `pretrained-ocr-v2.0-2023-06-02`
"""
checkboxes
=
[
x
for
x
in
visual_elements
if
"checkbox"
in
x
.
type
]
math_symbols
=
[
x
for
x
in
visual_elements
if
x
.
type
==
"math_formula"
]
if
checkboxes
:
print
(
f
"
{
len
(
checkboxes
)
}
checkboxes detected:"
)
print
(
f
" First checkbox:
{
repr
(
checkboxes
[
0
]
.
type
)
}
"
)
print
(
f
" Last checkbox:
{
repr
(
checkboxes
[
-
1
]
.
type
)
}
"
)
if
math_symbols
:
print
(
f
"
{
len
(
math_symbols
)
}
math symbols detected:"
)
first_math_symbol_text
=
layout_to_text
(
math_symbols
[
0
]
.
layout
,
text
)
print
(
f
" First math symbol:
{
repr
(
first_math_symbol_text
)
}
"
)
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
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