identifier1.py

import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf

### Model1

mnist = tf.keras.datasets.mnist
(x_train,y_train),(x_test,y_test) = mnist.load_data()

x_train = tf.keras.utils.normalize(x_train, axis=1)
x_test = tf.keras.utils.normalize(x_test,axis=1)

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28,28)))
model.add(tf.keras.layers.Dense(128,activation='relu'))
model.add(tf.keras.layers.Dense(128,activation='relu'))
model.add(tf.keras.layers.Dense(10,activation='softmax'))

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',metrics=['accuracy'])
model.fit(x_train, y_train, epochs = 15)

model.save('handwritten.model1')

model = tf.keras.models.load_model('handwritten.model1')
image_number = 0

while os.path.isfile(f"digits/{image_number}.jpg"):
    try:
        img = cv2.imread(f"digits/{image_number}.jpg")[:,:,0]
        img = np.invert(np.array([img]))
        prediction = model.predict(img)
        print(f"The number is probably a {np.argmax(prediction)}")
        plt.imshow(img[0], cmap=plt.cm.binary)
        plt.show()
    except:
        print("An exception occurred")
    finally:
        image_number += 1