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특수 프로세서를 사용하여 문서 처리

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처리 응답 처리

코드 샘플

Java

자세한 내용은 Document AI Java API 참고 문서를 참고하세요.

Document AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.


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.concurrent.ExecutionException;
import java.util.concurrent.TimeoutException;

public class ProcessSpecializedDocument {
  public static void processSpecializedDocument()
      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";
    processSpecializedDocument(projectId, location, processerId, filePath);
  }

  public static void processSpecializedDocument(
      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 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.
      for (Document.Entity entity : documentResponse.getEntitiesList()) {
        // Fields detected. For a full list of fields for each processor see
        // the processor documentation:
        // https://cloud.google.com/document-ai/docs/processors-list
        String entityType = entity.getType();
        // some other value formats in addition to text are availible
        // e.g. dates: `entity.getNormalizedValue().getDateValue().getYear()`
        // check for normilized value with `entity.hasNormalizedValue()`
        String entityTextValue = escapeNewlines(entity.getTextAnchor().getContent());
        float entityConfidence = entity.getConfidence();
        System.out.printf(
            "    * %s: %s (%.2f%% confident)\n",
            entityType, entityTextValue, entityConfidence * 100.0);
      }
    }
  }

  private static String escapeNewlines(String s) {
    return s.replace("\n", "\\n").replace("\r", "\\r");
  }
}

Node.js

자세한 내용은 Document AI Node.js API 참고 문서를 참고하세요.

Document AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

/**
 * 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;
  for (const entity of document.entities) {
    // Fields detected. For a full list of fields for each processor see
    // the processor documentation:
    // https://cloud.google.com/document-ai/docs/processors-list
    const key = entity.type;
    // some other value formats in addition to text are availible
    // e.g. dates: `entity.normalizedValue.dateValue.year`
    const textValue =
      entity.textAnchor !== null ? entity.textAnchor.content : '';
    const conf = entity.confidence * 100;
    console.log(
      `* ${JSON.stringify(key)}: ${JSON.stringify(textValue)}(${conf.toFixed(
        2
      )}% confident)`
    );
  }
}

Python

자세한 내용은 Document AI Python API 참고 문서를 참고하세요.

Document AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.


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_entity_extraction_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
    )

    # Print extracted entities from entity extraction processor output.
    # 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 processor's response.
    # Refer to the OCR samples for how to parse other data in the response.

    print(f"Found {len(document.entities)} entities:")
    for entity in document.entities:
        print_entity(entity)
        # Print Nested Entities (if any)
        for prop in entity.properties:
            print_entity(prop)




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

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코드 샘플

Java

Node.js

Python

다음 단계

특수 프로세서를 사용하여 문서 처리