Handwritten Digit Recognition In Python With Source Code
The Handwritten Digit Recognition In Python was developed using Python Deep Learning, This 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.
A Handwritten Digit Recognition s 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.
In this Python Project it also includes a downloadable Project With Source Code for free, just find the downloadable source code below and click to start downloading.
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To start executing Handwritten Digit Recognition In Python With Source Code, make sure that you have installed Python 3.9 and PyCharm on your computer.
Handwritten Digit Recognition In Python With Source Code : Steps on how to run the project.
Time needed: 5 minutes.
These are the steps on how to run Handwritten Digit Recognition In Python With Source Code
- Step 1: Download the given source code below.
First, download the given source code below and unzip the source code.
- Step 2: Import the project to your PyCharm IDE.
Next, import the source code you’ve download to your PyCharm IDE.
- Step 3: Run the project.
last, run the project with the command “py main.py”
from keras.models import load_model from tkinter import * import tkinter as tk import win32gui from PIL import ImageGrab, Image import numpy as np
Complete Source Code
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="Draw..", 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 a,b,c,d = rect rect=(a+4,b+4,c-4,d-4) 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()
Download Source Code below
In this article, we have successfully built a Python deep learning project on handwritten digit recognition app. We have built and trained the Convolutional neural network which is very effective for image classification purposes. Later on, we build the GUI where we draw a digit on the canvas then we classify the digit and show the results.
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