Energy performance diagnostic OCR
This article walks you through the building process of an OCR API that extracts data from energy performance diagnostics using our deep learning engine. It will work for any kind of EPD template.
- You’ll need a free account. Sign up and confirm your email to login.
- You’ll need at least 20 energy performance diagnostic images or pdfs to train your OCR.
Define your Energy performance diagnostic (DPE) use case
First, we’re going to define what fields we want to extract from your energy performance diagnostic.
- validation date: 30/06/2022
- annual energy consumption : 43430 KW/h
- habitation energy rating : D
- address: 12 Basse Rue, 85200 SAINT MARTIN DE FREIGNEAU
- annual energy price: 3063€
That’s it for our use case. Feel free to add any other relevant data to fit your requirements.
Deploy your API
Once you have defined the list of fields you want to extract, head over to the platform and press the ‘Create a new API’ button.
You land now on the setup page. Here is the image you can use for setting up the API, and my setup looks like this:
Once you’re ready, click on the “next” button. We are going to specify the data types for each of the fields we want our API to extract.
To go further, you can download this json config to set up your data model or do it manually.
validation date: type Date in the European default format
annual energy consumption: type Number/Integer
Habitation energy rating: type string contains only alpha characters
address: type string with both numeric and alpha characters.
annual energy price: Number type
Once you’re done setting up your data model, press the Start training your model button at the bottom of the screen.
Train your custom energy performance diagnostic OCR API
You’re all set!
Now is the time to train your DPE deep learning model in the Training section of our API.
You should get your first model trained in a few hours and you will be soon able to use your custom OCR API for parsing energy performance diagnostic in your application.
To get more information about the training phase, please refer to the Getting Started tutorial. If you have any questions regarding your use case, feel free to reach out on our chat!