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Taiwan National Treasure Backend

Components

  • AWS Lambda: most backend services are deployed as individual lambda functions
  • AWS API Gateway: provides endpoints to connect to lambda functions
  • AWS DynamoDB: main (NoSQL) database
  • AWS S3: storage of images
  • AWS SQS: message queue
  • AWS ElasticSearch (Planned for 2017 Winter)
  • AWS Machine Leaning (Planned for 2017 Winter)

3rd Party services

GCP = Google Cloud Platform

  • GCP Vision API: for OCR from images
  • GCP Translate API: to translate OCR results from En -> Zh-TW
  • GCP Natural Language API: to extract entities from both EN/Zh-TW texts

Backend services

  1. ImageUpload
  • After getting the base64 image posted to the API Gateway endpoint, ImageUpload service saves it in S3 and creates an record in DynamoDB. It also sends a message to SQS requesting image resizing.
  1. ImageResizer
  • After picking up the image resizing request message from SQS, this service gets the original image from S3 and resizes them, updating the database with resized image urls.
  1. Vision
  • Whenever a new image is inserted in S3, the bucket is configured to trigger this service, which requests OCR results from Google Vision API, and sends translation & NLP-English (Natural Language Processing) requests to SQS.
  1. Translate
  • After picking up the translate request message from SQS, this service requests Google Translate API with Enlighs OCR results, updating the database with the translation text. Then it sends a NLP-ZH-TW request to SQS.
  1. NLP
  • After picking up the NLP request message from SQS, this service requests Google NLP API, updating the database with the entities list. These may be English or Zh-Tw entities depending upon request.

To set up dev environment

  • Sign up on both AWS and GCP
  • Get GCP credentials (a key json file) and project ID
  • Create your lambda functions, with the following caveat
  • Set the env variables relating to GCP for Vision, Translate, NLP services for your lambdas
  • Normally to deploy to a lambda function you npm install, zip everything and upload to the lambda function
  • However, for ImageResizer, Vision and NLP there's a build step after npm install, and it won't build correctly if you build locally on your Mac or Windows machines.
  • So, you need to either 1) build it inside a docker container with Amazon Machine Image (AMI) Linux or 2) build it on a AMI Linux EC2 instance. If this is too complex, ask this repo author (join@nltr.tw) for zipped node_modules files with the correct built files.

Future plans

  • Set up ElasticSearch to index all OCR, translate and NLP results.
  • Set up Machine Learning to better understand the classification and categorization relationship between documents, or to help recommend documents for users to browse.

License

Open Source MIT