Digit Recogniser using Single Layer Perceptron
Competition Description
MNIST ("Modified National Institute of Standards and Technology") is the de facto “hello world” dataset of computer vision. Since its release in 1999, this classic dataset of handwritten images has served as the basis for benchmarking classification algorithms. As new machine learning techniques emerge, MNIST remains a reliable resource for researchers and learners alike.
Practice Skills
Computer vision fundamentals including simple neural networks Classification methods such as SVM and K-nearest neighbors
What is Handwritten Digit Recognition?
The handwritten digit recognition is the ability of computers to recognize human handwritten digits. It is a hard task for the machine because handwritten digits are not perfect and can be made with many different flavors. The handwritten digit recognition is the solution to this problem which uses the image of a digit and recognizes the digit present in the image.
Here, the goal is to correctly identify digits from a dataset of tens of thousands of handwritten images.
Our image recognition process contains three steps:
Get images of drawn digits for training
Train the system to guess the numbers via training data
Test the system with new/unknown data