Verwenden Sie es StartDocumentAnalysis mit einem AWS SDK oder CLI - AWS SDKCode-Beispiele

Weitere AWS SDK Beispiele sind im Repo AWS Doc SDK Examples GitHub verfügbar.

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Verwenden Sie es StartDocumentAnalysis mit einem AWS SDK oder CLI

Die folgenden Codebeispiele zeigen, wie man es benutztStartDocumentAnalysis.

Beispiele für Aktionen sind Codeauszüge aus größeren Programmen und müssen im Kontext ausgeführt werden. Im folgenden Codebeispiel können Sie diese Aktion im Kontext sehen:

CLI
AWS CLI

Um mit der Analyse von Text in einem mehrseitigen Dokument zu beginnen

Das folgende start-document-analysis Beispiel zeigt, wie die asynchrone Analyse von Text in einem mehrseitigen Dokument gestartet wird.

Linux/macOS:

aws textract start-document-analysis \ --document-location '{"S3Object":{"Bucket":"bucket","Name":"document"}}' \ --feature-types '["TABLES","FORMS"]' \ --notification-channel "SNSTopicArn=arn:snsTopic,RoleArn=roleArn"

Windows:

aws textract start-document-analysis \ --document-location "{\"S3Object\":{\"Bucket\":\"bucket\",\"Name\":\"document\"}}" \ --feature-types "[\"TABLES\", \"FORMS\"]" \ --region region-name \ --notification-channel "SNSTopicArn=arn:snsTopic,RoleArn=roleArn"

Ausgabe:

{ "JobId": "df7cf32ebbd2a5de113535fcf4d921926a701b09b4e7d089f3aebadb41e0712b" }

Weitere Informationen finden Sie unter Erkennen und Analysieren von Text in mehrseitigen Dokumenten im Amazon Textract Developers Guide

Java
SDKfür Java 2.x
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen.

import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.textract.model.S3Object; import software.amazon.awssdk.services.textract.TextractClient; import software.amazon.awssdk.services.textract.model.StartDocumentAnalysisRequest; import software.amazon.awssdk.services.textract.model.DocumentLocation; import software.amazon.awssdk.services.textract.model.TextractException; import software.amazon.awssdk.services.textract.model.StartDocumentAnalysisResponse; import software.amazon.awssdk.services.textract.model.GetDocumentAnalysisRequest; import software.amazon.awssdk.services.textract.model.GetDocumentAnalysisResponse; import software.amazon.awssdk.services.textract.model.FeatureType; import java.util.ArrayList; import java.util.List; /** * Before running this Java V2 code example, set up your development * environment, including your credentials. * * For more information, see the following documentation topic: * * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html */ public class StartDocumentAnalysis { public static void main(String[] args) { final String usage = """ Usage: <bucketName> <docName>\s Where: bucketName - The name of the Amazon S3 bucket that contains the document.\s docName - The document name (must be an image, for example, book.png).\s """; if (args.length != 2) { System.out.println(usage); System.exit(1); } String bucketName = args[0]; String docName = args[1]; Region region = Region.US_WEST_2; TextractClient textractClient = TextractClient.builder() .region(region) .build(); String jobId = startDocAnalysisS3(textractClient, bucketName, docName); System.out.println("Getting results for job " + jobId); String status = getJobResults(textractClient, jobId); System.out.println("The job status is " + status); textractClient.close(); } public static String startDocAnalysisS3(TextractClient textractClient, String bucketName, String docName) { try { List<FeatureType> myList = new ArrayList<>(); myList.add(FeatureType.TABLES); myList.add(FeatureType.FORMS); S3Object s3Object = S3Object.builder() .bucket(bucketName) .name(docName) .build(); DocumentLocation location = DocumentLocation.builder() .s3Object(s3Object) .build(); StartDocumentAnalysisRequest documentAnalysisRequest = StartDocumentAnalysisRequest.builder() .documentLocation(location) .featureTypes(myList) .build(); StartDocumentAnalysisResponse response = textractClient.startDocumentAnalysis(documentAnalysisRequest); // Get the job ID String jobId = response.jobId(); return jobId; } catch (TextractException e) { System.err.println(e.getMessage()); System.exit(1); } return ""; } private static String getJobResults(TextractClient textractClient, String jobId) { boolean finished = false; int index = 0; String status = ""; try { while (!finished) { GetDocumentAnalysisRequest analysisRequest = GetDocumentAnalysisRequest.builder() .jobId(jobId) .maxResults(1000) .build(); GetDocumentAnalysisResponse response = textractClient.getDocumentAnalysis(analysisRequest); status = response.jobStatus().toString(); if (status.compareTo("SUCCEEDED") == 0) finished = true; else { System.out.println(index + " status is: " + status); Thread.sleep(1000); } index++; } return status; } catch (InterruptedException e) { System.out.println(e.getMessage()); System.exit(1); } return ""; } }
Python
SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen.

Starten Sie einen asynchronen Job, um ein Dokument zu analysieren.

class TextractWrapper: """Encapsulates Textract functions.""" def __init__(self, textract_client, s3_resource, sqs_resource): """ :param textract_client: A Boto3 Textract client. :param s3_resource: A Boto3 Amazon S3 resource. :param sqs_resource: A Boto3 Amazon SQS resource. """ self.textract_client = textract_client self.s3_resource = s3_resource self.sqs_resource = sqs_resource def start_analysis_job( self, bucket_name, document_file_name, feature_types, sns_topic_arn, sns_role_arn, ): """ Starts an asynchronous job to detect text and additional elements, such as forms or tables, in an image stored in an Amazon S3 bucket. Textract publishes a notification to the specified Amazon SNS topic when the job completes. The image must be in PNG, JPG, or PDF format. :param bucket_name: The name of the Amazon S3 bucket that contains the image. :param document_file_name: The name of the document image stored in Amazon S3. :param feature_types: The types of additional document features to detect. :param sns_topic_arn: The Amazon Resource Name (ARN) of an Amazon SNS topic where job completion notification is published. :param sns_role_arn: The ARN of an AWS Identity and Access Management (IAM) role that can be assumed by Textract and grants permission to publish to the Amazon SNS topic. :return: The ID of the job. """ try: response = self.textract_client.start_document_analysis( DocumentLocation={ "S3Object": {"Bucket": bucket_name, "Name": document_file_name} }, NotificationChannel={ "SNSTopicArn": sns_topic_arn, "RoleArn": sns_role_arn, }, FeatureTypes=feature_types, ) job_id = response["JobId"] logger.info( "Started text analysis job %s on %s.", job_id, document_file_name ) except ClientError: logger.exception("Couldn't analyze text in %s.", document_file_name) raise else: return job_id
SAP ABAP
SDKfür SAP ABAP
Anmerkung

Es gibt noch mehr dazu GitHub. Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen.

"Starts the asynchronous analysis of an input document for relationships" "between detected items such as key-value pairs, tables, and selection elements." "Create ABAP objects for feature type." "Add TABLES to return information about the tables." "Add FORMS to return detected form data." "To perform both types of analysis, add TABLES and FORMS to FeatureTypes." DATA(lt_featuretypes) = VALUE /aws1/cl_texfeaturetypes_w=>tt_featuretypes( ( NEW /aws1/cl_texfeaturetypes_w( iv_value = 'FORMS' ) ) ( NEW /aws1/cl_texfeaturetypes_w( iv_value = 'TABLES' ) ) ). "Create an ABAP object for the Amazon S3 object." DATA(lo_s3object) = NEW /aws1/cl_texs3object( iv_bucket = iv_s3bucket iv_name = iv_s3object ). "Create an ABAP object for the document." DATA(lo_documentlocation) = NEW /aws1/cl_texdocumentlocation( io_s3object = lo_s3object ). "Start async document analysis." TRY. oo_result = lo_tex->startdocumentanalysis( "oo_result is returned for testing purposes." io_documentlocation = lo_documentlocation it_featuretypes = lt_featuretypes ). DATA(lv_jobid) = oo_result->get_jobid( ). MESSAGE 'Document analysis started.' TYPE 'I'. CATCH /aws1/cx_texaccessdeniedex. MESSAGE 'You do not have permission to perform this action.' TYPE 'E'. CATCH /aws1/cx_texbaddocumentex. MESSAGE 'Amazon Textract is not able to read the document.' TYPE 'E'. CATCH /aws1/cx_texdocumenttoolargeex. MESSAGE 'The document is too large.' TYPE 'E'. CATCH /aws1/cx_texidempotentprmmis00. MESSAGE 'Idempotent parameter mismatch exception.' TYPE 'E'. CATCH /aws1/cx_texinternalservererr. MESSAGE 'Internal server error.' TYPE 'E'. CATCH /aws1/cx_texinvalidkmskeyex. MESSAGE 'AWS KMS key is not valid.' TYPE 'E'. CATCH /aws1/cx_texinvalidparameterex. MESSAGE 'Request has non-valid parameters.' TYPE 'E'. CATCH /aws1/cx_texinvalids3objectex. MESSAGE 'Amazon S3 object is not valid.' TYPE 'E'. CATCH /aws1/cx_texlimitexceededex. MESSAGE 'An Amazon Textract service limit was exceeded.' TYPE 'E'. CATCH /aws1/cx_texprovthruputexcdex. MESSAGE 'Provisioned throughput exceeded limit.' TYPE 'E'. CATCH /aws1/cx_texthrottlingex. MESSAGE 'The request processing exceeded the limit.' TYPE 'E'. CATCH /aws1/cx_texunsupporteddocex. MESSAGE 'The document is not supported.' TYPE 'E'. ENDTRY.