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Checklist for Forms Extraction

This section serves to provide a generic checklist for the most important aspects of a successful project in this space.

A good starting point is the Engineering Fundamentals Checklist from the CSE code-with-engineering-playbook

The problem

  • Can the problem to be solved be clearly and succintly defined? For example, 'We need to extract the following 6 fields from these n forms'
  • Can the success criteria be clearly defined? For example, 'If the following 6 fields can be extracted 80% of the time from these n forms, then our success criteria are met'.

The approach

  • Has a hypothesis driven approach been adopted? This is vital for success on any data driven project.

Please refer to How to Implement Hypothesis-Driven Development as a good example of applying the scientific method to ensure success for a data driven software project.

The steps of the scientific method are to:

  • Make observations
  • Formulate a hypothesis
  • Design an experiment to test the hypothesis
  • State the indicators to evaluate if the experiment has succeeded
  • Conduct the experiment
  • Evaluate the results of the experiment
  • Accept or reject the hypothesis
  • If necessary, make and test a new hypothesis

The data

  • Sufficient data exists so that the data may be split into training, test and validation sets.
  • The data available is representative of what is required to be predicted in the production environment.
  • Sufficient labelled or Ground Truth (GT) data exists so that a variety of techniques may be experimented with.
  • The data is accessible to the project team and is compliant with the organisation's security policies.
  • All regulatory constraints on data collection, analysis, or implementation are clear.

The team

  • Data skills exist within the team to be able to analyse and manipulate the data as needed.
  • At least two data resources are available during the analysis phase to validate each other's work and implement a variety of diverse experimental approaches.

Additional references

Fast AI has a fantastic comprehensive checklist for generic data projects.

Now refer to the Decision Guidance section to determine whether the Form Recognizer service is a good fit.