How to implement document tagging with AutoML

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This type of architecture is not just simple to follow, but also easy to deploy in production. All components are based on existing GCP products that are highly scalable, serverless, and can be directly put in production.

  1. Tagged document–You can use the AI Platform Data Labeling Service if you don’t already have annotated data.

  2. OCR & object detection–This can be done by Vision API and AutoML Vision Object Detection, a recent addition to the AutoML suite of products.

  3. Merge and feature processing–There are several different ways this can be done, like using a simple Jupyter notebook or a Python-based containerized solution.

  4. Entity recognition–This can be done by using Entity extraction, a new feature in AutoML Natural Language, a recent addition to the AutoML suite of products

  5. Post processing–This can be done in a similar fashion to feature processing.

The whole pipeline can be orchestrated using Cloud Composer, or can be deployed using Google Kubernetes Engine (GKE). However, some business problems, for e.g. building customized data ingestion pipeline to GCP, rules extraction from legal documents, redact sensitive information from the documents before parsing etc., require additional customizations that can be developed in addition to the above mentioned architecture. For such requirements you can contact our sales team for more details and help.

Value generation

Different ML solutions have their own business or technical benefits–and many of our customers have used solutions like this one to meet their objectives, whether it’s enhancing the user experience, decreasing operational costs, or reducing overall errors. Solutions like the one described in this post can be used across industries such as healthcare, financial services, media, and more. Here are just a few examples:

  • Automatically extracting knowledge from Electronic Health Records (EHR).
  • Key value pair generation from invoices.
  • Field fetching from financial documents.
  • Text understanding of customer complaints.
  • Tagging of bank checks, tickets, and other data.

What’s next

In this age of deep learning, solutions that simplify the training process, like transfer learning, are increasingly needed. The architecture described in this post has been successfully tested and deployed to work at scale, and makes it possible to digitize documents without needing thousands of annotated images for model training.

Data variability, however, is still an important factor in any machine learning-based solution. AutoML automatically solves a lot of basic problems for variance in data, making it possible for you to use as little as a few thousand images to train a custom model.

Helping customers process their documents fits perfectly with Google’s mission to organize the world’s information and make it universally accessible and useful. We hope that by sharing this post, we can inspire more organizations to look to the cloud. Tools like Cloud AutoML Vision, Cloud AutoML Natural Language, and Cloud Storage can help you build a rich data set and improve the end-user experience.

This is a simple and targeted solution for a specific problem. For broader and more powerful document process automation and insight extraction technology, please refer to Google’s Document Understanding AI solution. AutoML is a core component of the end-to-end Document Understand AI solution, which is easy to deploy through our partners, and requires no machine learning expertise. You can learn more on our website.

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