[{"data":1,"prerenderedAt":37},["ShallowReactive",2],{"repo-amazon-serverless-document-processing":3},{"id":4,"title":5,"contributors":6,"date":8,"description":9,"diagram":10,"extension":11,"framework":12,"gitHub":14,"introBox":17,"language":21,"meta":23,"resources":24,"services":25,"simplicity":32,"stem":33,"type":34,"usecase":35,"__hash__":36},"repos\u002Frepos\u002Famazon-serverless-document-processing.json","Serverless Intelligent Document Processing",[7],"content\u002Fcontributors\u002Fpaul-kannan.json","2023-10-25","Identifying valid ID cards from uploaded documents","\u002Fassets\u002Fimages\u002Frepos\u002Famazon-serverless-document-processing.png","json",[13],"AWS CDK",{"template":15},{"repoURL":16},"https:\u002F\u002Fgithub.com\u002Faws-samples\u002Famazon-serverless-document-processing",{"headline":5,"text":18},[19,20],"This architecture is designed to provide a serverless, efficient, and automated solution for identifying valid Indian ID cards from uploaded documents into S3 and through API Gateway. The application supports identification and validation of Aadhaar, Indian Driving License, Indian Passport and Permanent Account Number in .jpg, .jpeg, and .png file formats only.","The workflow comprises three main phases: Image Validation by Amazon Rekognition, Data Extraction by Amazon Textract, and PII Entity Identification by Amazon Comprehend, followed by document categorization into valid and invalid ID cards. A notification is sent to end users through Amazon SNS, and the data is forwarded to Amazon SQS for consumption by end user applications.",[22],"Python",{},null,[26,27,28,29,30,31],"apigw","lambda-event-functions","rekognition","s3","sns","sqs","300","repos\u002Famazon-serverless-document-processing","Application","Backend","mACrHxiGB2sUBRsPJj6oCj-fx_JoCOCc3C63_LcSkx0",1792578864587]