[{"data":1,"prerenderedAt":55},["ShallowReactive",2],{"workflow-expense-analysis-workflow":3},{"id":4,"title":5,"cleanup":6,"contributors":10,"deploy":12,"description":16,"diagram":17,"extension":18,"framework":19,"gitHub":20,"introBox":29,"level":35,"meta":36,"resources":37,"s3URL":40,"services":41,"simplicity":46,"stem":47,"testing":48,"type":52,"usecase":53,"videoId":28,"__hash__":54},"workflows\u002Fworkflows\u002Fexpense-analysis-workflow.json","Expense analysis workflow",{"headline":7,"text":8},"Cleanup",[9],"1. Delete the stack: \u003Ccode>terraform destroy\u003C\u002Fcode>.",[11],"content\u002Fcontributors\u002Florenzo-micheli.json",{"text":13},[14,15],"terraform init","terraform apply","Analyze the picture of an expense receipt stored in Amazon S3 using the AnalyzeExpense API call of Amazon Textract. The extracted expense data is persisted in DynamoDB.","\u002Fassets\u002Fimages\u002Fworkflows\u002Fexpense-analysis-workflow.png","json","Terraform",{"template":21,"payloads":26},{"repoURL":22,"templateDir":23,"templateFile":24,"ASL":25},"https:\u002F\u002Fgithub.com\u002Faws-samples\u002Fstep-functions-workflows-collection\u002Ftree\u002Fmain\u002Ftextract-analyze-expense-tf\u002F","textract-analyze-expense-tf\u002F","main.tf","statemachine\u002Fstatemachine.asl.json",[27],{"headline":28,"payloadURL":28},"",{"headline":30,"text":31},"How it works",[32,33,34],"Uploading an image of a receipt in the provided S3 bucket will trigger the workflow.","The workflow uses Amazon Rekognition to detect if the image is a receipt. If the Receipt label is found and it has a confidence score greater than 80, then it uses Amazon Textract to analyze the expense","The extracted invoce data is saved in DynamoDb.","200",{},{"headline":38,"bullets":39},"Additional resources",[],null,[42,43,44,45],"textract","rekognition","s3","dynamodb","3 - Application","workflows\u002Fexpense-analysis-workflow",{"headline":49,"text":50},"Testing",[51],"See the GitHub repo for detailed testing instructions.","Standard","Data Processing","rpu3UMiPxMuw2-rRLQfyj5wx_qCuh4U3jAMGVTt970I",1779273340762]