Handwritten math symbols
Handwritten math symbols
BUILD AN AI MODEL THAT CAN CLASSIFY HANDWRITTEN MATH SYMBOLS
SOLVING A CLASSIFICATION PROBLEM
We have come a long way since the paper was the only storage medium available to us. Now, digital storage is mainstream. However, much information is still in handwritten form while some ancient documents exist in this form too.
In this project, we create a model that can recognize handwritten mathematics documents.
By taking on classification problems, humans can understand the world better. The application of this technique in the real-world is a rather more challenging task because of the fine lines between classes. However, AI makes classification despite these fine lines easier and faster.
In this challenge, we take on the task of building a model that can differentiate handwritten mathematical symbols.
WHAT IS IMAGE CLASSIFICATION?
Although you will learn more about classification in the second course of Level2 Supervised Learning, in the AI Master Program, here’s a brief idea: classification is a technique under supervised learning in which a model takes in an input (here, satellite images), learns from the input and then proceeds to classify any new input it is fed with.
From the knowledge you gain from this project, you will grasp how (to):
- Gather data and divide it into training and testing datasets.
- Create an ML model to classify handwritten mathematical symbols.
The dataset here is a set of handwritten math symbols with which you will train an ML model to classify a new set of mathematical symbols.
Be sure to split the dataset into training and testing datasets to ensure the model’s accurate performance.
PROJECTS STEPS: 1. Meet Your AI Identity
Meet your AI Identity. You will teach your AI how to complete tasks as a human would. In due time, your AI Identity will grow more intelligent as you train it some more to take on different tasks with multiple projects. Eventually -in the future- you will be able to transfer your AI identity's intellect into a physical robot you can interact with in the real world.
2. DEFINE THE PROBELM. START YOUR PROJECT
Begin every project by naming the project so you can keep track of what project it is exactly. Every project involves the steps of dataset preprocessing, model building, evaluation, and deployment.
By creating projects, working with others becomes more convenient and easier.
Click CREATE once you have added a project name and description.
3. SELECT YOUR ML MODEL
Here, from the options of a Computer Vision model (MAKE ME SEE), Natural Language Processing model (MAKE ME READ), Voice Recognition (MAKE ME HEAR), and a tabular model (MAKE ME COUNT), you will select the first i.e. MAKE ME SEE.
This model will enable you to train your AI transformer to recognize and classify handwritten mathematical symbols.
Keep track of the newer models that will be added to the platform. Each one of these models will enable your AI transformer to take on and solve new problems.
More AI capabilities (models) will be added to the platform, so stay tuned! These models will help you to accelerate the process of creating a trained AI model that solves your problem, and thus export into your app to start using it!
4. COLLECT YOUR DATA
Use either the “Handwritten Math Symbols” images dataset from the dataset library or manually source reviews from recognized sources such as Kaggle to collect the images you need for this project.
Take note that data gathering is a very crucial stage of an ML project with data preprocessing taking at least 70% of the time. Therefore, you will need to carry out this stage well.
5. TRAIN YOUR MODEL
Use the images you have gathered to train your AI transformer. “Training” your model refers to teaching it to recognize and classify handwritten math symbols. The aim of this is so the AI transformer can categorize new handwritten symbols to determine if it is a mathematical symbol.
To do this, click TRAIN ME. Be patient for a moment while this process goes on.
6. TEST YOUR MODEL
With your AI model trained now, it is time to test the model by feeding it new handwritten symbols. In this testing phase, take the precaution of not using any image that was used during the training phase.
The PREVIEW section will serve to determine how well the model has been trained. All you have to do is upload/import the new images to this section. The confidence score will help you know well the model has been trained and if you are fine with it, export the model.
If you do not like the confidence score, return to step 4, and determine whether enough images were used to train the model.
EXPORT YOUR MODEL
Exporting your model is the next step after you are satisfied with the confidence score. You can export using our API into your website, application, robot, or any other machine.
Return to step 4 if you got a low confidence score. Doing this helps you review the training data class to be sure it is of high quality (clean) and quantity.
Your model is only as good as the data you feed it.