向 Form Parser 处理器发送在线处理请求并解析响应。 提取和打印表单字段和表格。
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如需查看包含此代码示例的详细文档，请参阅以下内容：
代码示例
Java
如需了解详情，请参阅 Document AI Java API 参考文档。
如需向 Document AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
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
如需了解详情，请参阅 Document AI Node.js API 参考文档。
如需向 Document AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
/**
* 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
如需了解详情，请参阅 Document AI Python API 参考文档。
如需向 Document AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
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"\nFound {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"\nFound {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
)
后续步骤
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