To make machines more intelligent, developers are diving into machine learning and deep learning techniques. A human learns to perform a task by practicing and repeating it again and again so that it memorizes how to perform the tasks. Then the neurons in his brain automatically trigger and they can quickly perform the task they have learned. Deep learning is also very similar to this. It uses different types of neural network architectures for different types of problems. For example – object recognition, image and sound classification, object detection, image segmentation, etc.
What is Handwritten Digit Recognition?
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. Handwritten digit recognition is the solution to this problem which uses the image of a digit and recognizes the digit present in the image.
About the Python Deep Learning Project
In this article, we are going to implement a handwritten digit recognition app using the MNIST dataset. We will be using a special type of deep neural network that is Convolutional Neural Networks. In the end, we are going to build a GUI in which you can draw the digit and recognize it straight away.
The interesting Python project requires you to have basic knowledge of Python programming, deep learning with Keras library, and the Tkinter library for building GUI.
Install the necessary libraries for this project using this command:
pip install numpy, tensorflow, keras, pillow,
The MNIST dataset
This is probably one of the most popular datasets among machine learning and deep learning enthusiasts. The MNIST dataset contains 60,000 training images of handwritten digits from zero to nine and 10,000 images for testing. So, the MNIST dataset has 10 different classes. The handwritten digits images are represented as a 28×28 matrix where each cell contains a grayscale pixel value.
Building Python Deep Learning Project on Handwritten Digit Recognition
Below are the steps to implement the handwritten digit recognition project:
1. Import the libraries and load the dataset
First, we are going to import all the modules that we are going to need for training our model. The Keras library already contains some datasets and MNIST is one of them. So we can easily import the dataset and start working with it. The mnist.load_data() method returns us the training data, its labels, and also the testing data and its labels.
import keras from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D from keras import backend as K # the data, split between train and test sets (x_train, y_train), (x_test, y_test) = mnist.load_data() print(x_train.shape, y_train.shape)
2. Preprocess the data
The image data cannot be fed directly into the model so we need to perform some operations and process the data to make it ready for our neural network. The dimension of the training data is (60000,28,28). The CNN model will require one more dimension so we reshape the matrix to shape (60000,28,28,1).
x_train = x_train.reshape(x_train.shape, 28, 28, 1) x_test = x_test.reshape(x_test.shape, 28, 28, 1) input_shape = (28, 28, 1) # convert class vectors to binary class matrices y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) x_train = x_train.astype('float32') x_test = x_test.astype('float32') x_train /= 255 x_test /= 255 print('x_train shape:', x_train.shape) print(x_train.shape, 'train samples') print(x_test.shape, 'test samples')
3. Create the model
Now we will create our CNN model in Python data science project. A CNN model generally consists of convolutional and pooling layers. It works better for data that are represented as grid structures, this is the reason why CNN works well for image classification problems. The dropout layer is used to deactivate some of the neurons and while training, it reduces the offer fitting of the model. We will then compile the model with the Adadelta optimizer.
batch_size = 128 num_classes = 10 epochs = 10 model = Sequential() model.add(Conv2D(32, kernel_size=(3, 3),activation='relu',input_shape=input_shape)) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(256, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(num_classes, activation='softmax')) model.compile(loss=keras.losses.categorical_crossentropy,optimizer=keras.optimizers.Adadelta(),metrics=['accuracy'])
4. Train the model
The model.fit() function of Keras will start the training of the model. It takes the training data, validation data, epochs, and batch size.
It takes some time to train the model. After training, we save the weights and model definition in the ‘mnist.h5’ file.
hist = model.fit(x_train, y_train,batch_size=batch_size,epochs=epochs,verbose=1,validation_data=(x_test, y_test)) print("The model has successfully trained") model.save('mnist.h5') print("Saving the model as mnist.h5")
5. Evaluate the model
We have 10,000 images in our dataset which will be used to evaluate how well our model works. The testing data was not involved in the training of the data therefore, it is new data for our model. The MNIST dataset is well balanced so we can get around 99% accuracy.
score = model.evaluate(x_test, y_test, verbose=0) print('Test loss:', score) print('Test accuracy:', score)
6. Create GUI to predict digits
Now for the GUI, we have created a new file in which we build an interactive window to draw digits on canvas and with a button, we can recognize the digit. The Tkinter library comes in the Python standard library. We have created a function predict_digit() that takes the image as input and then uses the trained model to predict the digit.
Then we create the App class which is responsible for building the GUI for our app. We create a canvas where we can draw by capturing the mouse event and with a button, we trigger the predict_digit() function and display the results.
Here’s the full code for our gui_digit_recognizer.py file:
from keras.models import load_model from tkinter import * import tkinter as tk import win32gui from PIL import ImageGrab, Image import numpy as np model = load_model('mnist.h5') def predict_digit(img): #resize image to 28x28 pixels img = img.resize((28,28)) #convert rgb to grayscale img = img.convert('L') img = np.array(img) #reshaping to support our model input and normalizing img = img.reshape(1,28,28,1) img = img/255.0 #predicting the class res = model.predict([img]) return np.argmax(res), max(res) class App(tk.Tk): def __init__(self): tk.Tk.__init__(self) self.x = self.y = 0 # Creating elements self.canvas = tk.Canvas(self, width=300, height=300, bg = "white", cursor="cross") self.label = tk.Label(self, text="Thinking..", font=("Helvetica", 48)) self.classify_btn = tk.Button(self, text = "Recognise", command = self.classify_handwriting) self.button_clear = tk.Button(self, text = "Clear", command = self.clear_all) # Grid structure self.canvas.grid(row=0, column=0, pady=2, sticky=W, ) self.label.grid(row=0, column=1,pady=2, padx=2) self.classify_btn.grid(row=1, column=1, pady=2, padx=2) self.button_clear.grid(row=1, column=0, pady=2) #self.canvas.bind("<Motion>", self.start_pos) self.canvas.bind("<B1-Motion>", self.draw_lines) def clear_all(self): self.canvas.delete("all") def classify_handwriting(self): HWND = self.canvas.winfo_id() # get the handle of the canvas rect = win32gui.GetWindowRect(HWND) # get the coordinate of the canvas im = ImageGrab.grab(rect) digit, acc = predict_digit(im) self.label.configure(text= str(digit)+', '+ str(int(acc*100))+'%') def draw_lines(self, event): self.x = event.x self.y = event.y r=8 self.canvas.create_oval(self.x-r, self.y-r, self.x + r, self.y + r, fill='black') app = App() mainloop()