Nutrition Facts Label

This section describes how to build your custom OCR API to extract data from Nutrition Facts Label using the API Builder. A Nutrition Facts Label lists the nutritional content, the serving size, and the calories for a recommended serving of a food product

Prerequisites

You’ll need at least 20 nutrition facts labels or pdfs to train your OCR.

Define Your Nutrition Fact Label Use Case

Using the Nutrition Fact Label below, we’re going to define the fields we want to extract from it.

Nutrition Fact LabelNutrition Fact Label

  • Serving size: 170 gram
  • Total fat gram: 2 gram
  • Total fat daily value : 3%
  • Calories per serving: 150

That’s it for this example. Feel free to add any other relevant data that fits your requirement.

Deploy your API

Once you have defined the list of fields you want to extract from your Nutrition Facts Label, head over to the platform and follow these steps:

  1. Click on the Create a new API button on the right.

  2. Next, fill in the basic information about the API you want to create as seen below.

Set up your APISet up your API

  1. Click on the Next button. The following page allows you to define and add your data model.

Define Your Model

There are two ways to add fields to your data model.

  • Upload a JSON config file
  • Manually add data

Data ModelData Model

Upload a JSON Config

To add data fields using JSON config upload.

  1. Copy the following JSON into a file.
{
  "problem_type": {
    "classificator": { "features": [], "features_name": [] },
    "selector": {
      "features": [
        {
          "cfg": { "filter": { "is_integer": 1 } },
          "handwritten": false,
          "name": "serving_size",
          "public_name": "Serving size",
          "semantics": "amount"
        },
        {
          "cfg": { "filter": { "is_integer": -1 } },
          "handwritten": false,
          "name": "total_fat_gram",
          "public_name": "Total fat gram",
          "semantics": "amount"
        },
        {
          "cfg": { "filter": { "alpha": 0, "numeric": -1 } },
          "handwritten": false,
          "name": "total_fat_daily_value",
          "public_name": "Total fat daily value",
          "semantics": "word"
        },
        {
          "cfg": { "filter": { "is_integer": 1 } },
          "handwritten": false,
          "name": "calories_per_serving",
          "public_name": "Calories per serving",
          "semantics": "amount"
        }
      ],
      "features_name": [
        "serving_size",
        "total_fat_gram",
        "total_fat_daily_value",
        "calories_per_serving"
      ]
    }
  }
}
  1. Click on Upload a JSON config.
  2. The data model will be automatically filled.
  3. Click on Create API at the bottom of the screen.

Document Data Model filledDocument Data Model filled

Manually Add Data

Using the interface, you can manually add each field for the data you are extracting. For this example, here are the different field configurations used:

  • Serving size: type Integer
  • Total fat gram: type Number
  • Total fat daily value: type String that never contains alpha characters
  • Calories per serving: type Integer

Once you’re done setting up your data model, click the Create API button at the bottom of the screen.

Document Data Model filledDocument Data Model filled

Train Your Nutrition Facts Labels OCR

You’re all set! Now it's time to train your Nutrition Facts Labels deep learning model in the Training section of our API.

Train your modelTrain your model

  1. Upload one file at a time or a zip bundle of many files.
  2. Click on the field input on the right, and the blue box on the left highlights all the corresponding field candidates in the document.
  3. Next, click on the validate arrow for all the field inputs.
  4. Once you have selected the proper box(es) for each of your fields as displayed on the right-hand side, click on the validate button located at the right-side bottom to send an annotation for the model you have created.
  5. Repeat this process until you have trained 20 documents to create a trained model.

To get more information about the training phase, please refer to the Getting Started tutorial.

 
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