handwritten-digit-recognition-using-convnets-keras-theano
Handwritten digit recognition using convolutional neural networks python keras theano
Computer vision is a field of computer science that works on enabling computers to see, identify and process images in the same way that human vision does, and then provide appropriate output.
Computer vision is closely linked with artificial intelligence, as the computer must interpret what it sees, and then perform appropriate analysis or act accordingly and provide useful results based on the observation.
Computer vision is like imparting human intelligence and instincts to a computer. In reality though, it is a difficult task to enable computers to recognize images of different objects. The difficulty of visual pattern recognition becomes apparent if you attempt to write a computer program to recognize handwritten digits. What seems easy when we do it ourselves suddenly becomes extremely difficult.
MNIST is a modified subset of two datasets collected by the U.S. National Institute of Standards and Technology. It contains 70,000 labeled images of handwritten digits. The MNIST database of handwritten digits, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255, inclusive.
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing.
A neural network is a biologically-inspired programming paradigm which enables a computer to learn from observational data. This artificial intelligence technique mimics the operation of the human brain and comprises of densely interconnected computer processors working simultaneously. The key feature of neural networks is that they are programmed to 'learn' by sifting data repeatedly, looking for relationships to build mathematical models, and automatically correcting these models to refine them continuously
Deep learning is a powerful set of techniques for learning and implementing the neural networks.
There are multiple historical models are available in public domain to compare results and provide benchmark.