Qr Code Machine Learning (Developer Tutorial)

Updated August 29, 2023

Quick Response (QR) codes have become an integral part of modern society, revolutionizing the way information is shared and accessed. These two-dimensional barcodes consist of a matrix of black and white squares that can be scanned using a smartphone camera to quickly retrieve information stored, such as URLs, contact details, website URLs, product details, and more. However, as technology evolves, so do the capabilities of QR codes. Machine Learning (ML) has emerged as a powerful tool in enhancing QR code generation, recognition, and decoding processes, leading to improved user experiences and greater functionality.

The world of information and data exchange has been transformed by the widespread use of QR codes and barcodes. These two-dimensional codes provide quick access to digital content and serve various purposes across industries. However, distinguishing between QR codes and barcodes can be challenging, especially when dealing with a diverse range of codes. By using the power of Machine Learning (ML) and the .NET/C# framework, developers can create an intelligent system that accurately classifies QR codes and barcodes, streamlining data processing and enhancing user experiences. Integrating IronBarcode's decoding capabilities further enhances the solution, enabling accurate content extraction from the recognized codes.

In this article, we will train the model which will classify the given input as Barcode or QR Code, and after that, we will decode the Input code using IronBarcode.

Using ML.NET Model Builder for QR Code Classification

Model Builder, an integrated tool within Visual Studio, simplifies the process of building Machine Learning models by providing an intuitive interface. We will use Model Builder to create a classification model that distinguishes between QR codes and barcodes. Once the classification is complete, we will integrate IronBarcode to decode the recognized codes.

Step 1: Data Collection

The very first step for training any kind of model is data collection. We need data for training the Model. I have downloaded data from Kaggle. You may download it from any source as per your preference. After downloading the dataset, create a main directory and put both datasets inside that main directory. The data is organized into two subdirectories:

  1. Barcodes
  2. QR codes

QR Code Machine Learning (Developer Tutorial): Figure 1

The next step is to open Visual Studio and create a new Machine Learning Model.

Step #2: Create Project in Visual Studio and Add a new Machine Learning Model

Create your Project in Visual Studio. The project can be of any type, such as ASP.NET Web API, ASP.NET MVC, WEB Forms, or a console application. In this example, I am using a console application. Right-click on the Project from Solution Explorer. Select Add > Machine Learning Model as shown below.

QR Code Machine Learning (Developer Tutorial): Figure 2

Name your Machine learning model and click on the Add Button.

QR Code Machine Learning (Developer Tutorial): Figure 3

Step #3: Select Scenario

A new window will appear after adding the Machine learning model file. Scroll down below and select Image Classification. Image Classification is one of the Computer vision techniques that we are going to use in this tutorial. We are selecting Image classification instead of Image recognition since we only need to differentiate between Quick Response Code (QR Code) and Barcode, we don't need to recognize them from a given image.

QR Code Machine Learning (Developer Tutorial): Figure 4

Step #4: Select Environment

The very next step after selecting a scenario is to choose the environment. We can choose either our local system to train the model or Azure Cloud. We must have an active subscription to Azure for using Azure as our environment.

Step #5: Select Data

In this step, we need to pass the data that we have collected before in step #1. We have already organized the data into subcategories in step #1 as required by the Model Builder.

Provide the path of the main directory of your dataset in the Select Folder option. We can preview our data as shown below.

QR Code Machine Learning (Developer Tutorial): Figure 5

We can also view the QR Code folder by clicking on the QR codes tab in the data preview.

QR Code Machine Learning (Developer Tutorial): Figure 6

After selecting data, click on the Next button, it will take you to the training screen.

Step #6: Train the Model

Click on Start Training Model, and it will start training as shown below. Please make sure that Visual Studio is not open with Administrator Mode, else it will throw an error.

QR Code Machine Learning (Developer Tutorial): Figure 7

It will start training and will take time. The training duration will vary depending on system capabilities. Model Builder uses AutoML. It will automatically use suitable machine learning techniques, error correction level, recognize patterns, noise reduction, contrast adjustment, learning process, and other settings accordingly.

Step #7: Model Evaluation

After training, evaluate the model's performance by passing the test data to ensure accurate classification.

Step #8: Consume Model

Once the model is trained and evaluated, we can export and consume it. In our use case, we have to classify the QR Code from the Barcode and then read its encoded data. We need a third-party library for decoding data since the machine learning model cannot decode the QR code or barcode, it will just classify it.

IronBarcode is an ideal library for decoding both QR Code and Barcode. We can decode both types of codes using a single library in a straightforward way. Let's have a little introduction about IronBarcode before proceeding further.

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