Do you ever wish you could have an AI read your messy handwriting and convert it into typed text? Well, you’re in luck! In this Crash Course AI lab, we will walk you through the process of training a neural network to recognize handwritten letters. So, let’s get started!
Contents
Step 1: Creating a Labeled Dataset
Before we can train our neural network, we need a dataset of labeled images. Luckily, there is a dataset called EMNIST (Extended Modified National Institute of Standards and Technology) that contains tens of thousands of labeled images of handwritten letters. We will use the EMNIST letters chunk of the dataset, which has 145,600 images of letters.
To ensure accurate training and testing, we split the dataset into a training set and a testing set. The training set is used to train the neural network, while the testing set is used to evaluate the network’s accuracy. For this lab, we will focus on recognizing letters, not numbers.
Step 2: Creating a Neural Network
To build our neural network, we will use the Multi-Layer Perceptron (MLP) algorithm from the SKLearn library. Our network will have an input layer with 784 neurons (corresponding to the 28×28 pixel size of each image), a single hidden layer with 50 neurons, and an output layer with 26 neurons (representing the 26 letters in the English alphabet).
Step 3: Training the Neural Network
Now it’s time to train our neural network. We will train it over 20 epochs, which means the network will process each image in the training set 20 times. As the network trains, it will compare its predictions with the actual labels and adjust its weights and biases to improve its accuracy. After training, we can evaluate the network’s performance on the testing set.
Step 4: Converting Handwritten Pages to Typed Text
The moment of truth has arrived! We will use our trained neural network to process handwritten pages and convert them into typed text. We need to scan the pages and ensure they are in the correct format. We have preprocessed the scanned images by resizing them to 128×128 pixels and inverting the colors to match the EMNIST dataset.
However, we encountered some challenges. Empty spaces between words and blank pages were not included in our training, so we need to address those. We will check each image for blank spaces and add a space to our story accordingly. Additionally, we discovered that further processing is needed to make the scanned images more similar to the EMNIST dataset.
After implementing these adjustments, we can successfully convert the handwritten story into typed text, although some errors may still occur. With an 88% accuracy rate, we can expect approximately 1 in 15 letters to be incorrect.
Conclusion
Congratulations! You have learned how to train an AI to read handwriting using a neural network. This process involves creating a labeled dataset, building a neural network, training the network, and converting handwritten pages into typed text. While our model achieved an 88% accuracy rate, there is always room for improvement. Feel free to experiment and enhance the code to achieve even better results.
Remember, this lab focused on supervised machine learning, but there are other types of machine learning, such as unsupervised and reinforcement learning, which we will explore in future videos. Stay tuned for more exciting AI adventures!
To learn more about AI and its various applications, visit Techal.
FAQs
Q: Can we improve the accuracy of the neural network?
A: Yes, you can experiment with different network structures, such as adding more hidden layers or increasing the number of neurons in the hidden layers. Finding the optimal architecture for a specific task requires trial and error.
Q: How can we handle blank spaces and words in the handwritten pages?
A: Blank spaces between words can be detected by checking if an image is completely blank. You can add a space to the story when a blank image is detected. As for individual words, you would need additional processing techniques to segment the text and recognize each word separately.
Q: Are there other datasets available for training handwriting recognition models?
A: Yes, there are various datasets available, such as the MNIST dataset, which contains images of handwritten digits. You can also create your own dataset by collecting labeled images of handwritten letters or explore other publicly available datasets.
Q: How can I apply this knowledge to my own projects?
A: You can use the code and techniques presented in this lab as a starting point for your own handwriting recognition projects. Feel free to modify the code and experiment with different approaches to achieve better results.
Q: Are there alternative methods for converting handwriting to typed text?
A: Yes, there are other methods for handwriting recognition, including Optical Character Recognition (OCR) software and deep learning models. Each method has its own strengths and limitations, so it’s important to choose the approach that best suits your specific needs.
Conclusion
Congratulations! You have learned how to train an AI to read handwriting using a neural network. This process involves creating a labeled dataset, building a neural network, training the network, and converting handwritten pages into typed text. While our model achieved an 88% accuracy rate, there is always room for improvement. Feel free to experiment and enhance the code to achieve even better results.
Remember, this lab focused on supervised machine learning, but there are other types of machine learning, such as unsupervised and reinforcement learning, which we will explore in future videos. Stay tuned for more exciting AI adventures!
To learn more about AI and its various applications, visit Techal.
