Traffic Ticket

This article describes how to build an OCR API that extracts data from Traffic ticket using our deep learning engine. If you want to automate your traffic ticket workflow, this article is for you.

Prerequisites

You’ll need at least 20 Traffic ticket images or pdfs to train your OCR.

Define your Traffic Ticket Use Case

Using the Traffic Ticket below, we’re going to define the fields we want to extract from it.

• Date: The date of the offence
• Time: The time of the offence
• Description: The description of the violation
• Place of occurrence: The place where the violation occurred
• Badge: The badge number

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 Traffic Ticket, 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.

3. 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

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": { "convention": "US" } },
          "handwritten": false,
          "name": "date_of_offence",
          "public_name": "Date of Offence",
          "semantics": "date"
        },
        {
          "cfg": { "filter": { "alpha": -1, "numeric": -1 } },
          "handwritten": false,
          "name": "time",
          "public_name": "Time",
          "semantics": "word"
        },
        {
          "cfg": { "filter": { "alpha": -1, "numeric": -1 } },
          "handwritten": false,
          "name": "description_of_violation",
          "public_name": "Description of Violation",
          "semantics": "word"
        },
        {
          "cfg": { "filter": { "alpha": -1, "numeric": -1 } },
          "handwritten": false,
          "name": "place_of_occurrence",
          "public_name": "Place of Occurrence",
          "semantics": "word"
        },
        {
          "cfg": { "filter": { "is_integer": -1 } },
          "handwritten": false,
          "name": "badge_number",
          "public_name": "Badge Number",
          "semantics": "amount"
        }
      ],
      "features_name": [
        "date_of_offence",
        "time",
        "description_of_violation",
        "place_of_occurrence",
        "badge_number"
      ]
    }
  }
}

2. Click on Upload a JSON config.
3. The data model will be automatically filled.
4. Click on Create API at the bottom of the screen.

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:

• Date of Offence: type Date with US format
• Time: type String without specifications.
• Description of Violation: type String without specifications.
• Place of Occurrence: type String without specifications.
• Badge Number: type Number without specifications.

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

Train your Traffic Ticket OCR

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

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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