How to make an AI read your handwriting (LAB) : Crash Course Ai #5

Summary

In this Crash Course AI lab, host Jabril guides viewers through building a neural network to recognize handwritten letters using Python and Google Colab. They utilize the EMNIST dataset, preprocess images, and experiment with network configurations to improve accuracy. The lab concludes with an attempt to digitize a novel written by John-Green-Bot, highlighting the challenges and potential of AI in text recognition.

Takeaways

• 🤖 The script is a transcript from a Crash Course AI episode focused on building a neural network to recognize handwritten letters.
• 📚 They use the EMNIST dataset, which contains tens of thousands of labeled images of handwritten letters and numbers from US Census forms.
• 💡 The project aims to digitize a novel written by a bot named John-Green-Bot, which was handwritten with one letter per page.
• 🔍 The neural network is trained to recognize letters and convert them to typed text, bypassing the segmentation problem due to the novel's format.
• 💻 Programming is done in Python using Google Colaboratory, with code made accessible to viewers through a link in the video description.
• 📈 The training process involves creating a labeled dataset, building a neural network model, training and testing it, and then tweaking for accuracy.
• 🧠 The neural network starts with a simple structure and is gradually improved by adding more hidden layers and neurons, and increasing training epochs.
• 📊 A confusion matrix is used to visualize where the network makes mistakes, helping to identify patterns in mislabeled letters.
• 🖼️ Preprocessing of images includes normalization and resizing to match the dimensions used in the EMNIST dataset for better recognition.
• 🔎 The final step involves scanning and preprocessing the handwritten pages and using the trained network to convert them into digital text.
• 🔗 The episode concludes with a teaser for future episodes that will cover other types of machine learning beyond supervised learning.

Q & A

What is the main goal of the project described in the script?

The main goal of the project is to program a neural network to recognize handwritten letters and convert them to typed text.

Which dataset is used to train the neural network in the project?

The Extended Modified National Institute of Standards and Technology dataset, or EMNIST, is used to train the neural network.

What preprocessing steps are taken on the EMNIST dataset images?

The images are normalized by dividing each pixel value by 255 to give a number between 0 and 1 for each pixel.

Why is the segmentation problem avoided in this project?

The segmentation problem is avoided because John-Green-bot wrote his novel with one letter per page, which means the letters are already segmented.

What is the structure of the neural network used in the project?

The neural network used is a multi-layer perceptron with a single hidden layer containing 50 neurons, trained over 20 epochs.

How is the neural network's performance evaluated?

The neural network's performance is evaluated by its accuracy on the testing dataset, which contains data the network has never seen before.

What is a confusion matrix and how is it used in this project?

A confusion matrix is a table used to describe the performance of a classification model, where each cell represents the number of instances of a predicted class being correctly or incorrectly classified. It is used to see where the network made the most mistakes.

How does the script suggest improving the neural network's accuracy?

The script suggests improving the neural network's accuracy by trying different structures, such as more epochs, more hidden layers, and more neurons in the hidden layers.

What challenges are faced when trying to read John-Green-bot's novel with the trained neural network?

The challenges include the model not being trained on empty spaces, the scanned images being too large compared to the training samples, and the need to process the images in the same way as the EMNIST dataset.

What modifications are made to the scanned images of John-Green-bot's novel to improve the neural network's accuracy?

The modifications include resizing the images to 28x28 pixels, inverting the colors to match the EMNIST dataset, and applying filters to soften the letter edges and center the letters.

What is the final outcome of using the trained neural network on John-Green-bot's novel?

The final outcome is a digitized version of the novel with some inaccuracies, but the text is understandable with context and knowledge of which letters might be mistaken for each other.