Handwriting Recognition using RNN and TensorFlow
To practice RNNs, we will use the dataset previously used to construct the CNN. I refer to the MNIST dataset, a large database of handwritten digits. It has a set of 70,000 examples of data. It is a subset of NIST's larger dataset. Images of 28 x 28 pixel resolution are stored in a matrix of 70,000 rows and 785 columns; each pixel value from the 28 x 28 matrix and one value is the actual digit. In a fixed-size image, the digits have been size-normalized.
In this case, we will implement an RNN (LSTM) using the TensorFlow library to classify images. We will consider every image row as a sequence of pixels. Because the MNIST image shape is 28 x 28, we will handle 28 sequences of 28 time steps for every sample.
To start, we will analyze the code line by line; then we will see how to process it with the tools made available by Google Cloud Platform. Now, let's go through the code to learn how to apply an RNN (LSTM) to solve an HWR problem. Let's...
