当然可以！你可以使用Python中的机器学习库来实现这个功能。下面是一个使用深度学习库Keras和TensorFlow的示例代码：

“`python
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.datasets import mnist
from keras.utils import to_categorical

# 加载MNIST数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 数据预处理
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
input_shape = (28, 28, 1)

x_train = x_train.astype(‘float32’)
x_test = x_test.astype(‘float32’)
x_train /= 255
x_test /= 255

y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

# 构建模型
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation=’relu’, input_shape=input_shape))
model.add(Conv2D(64, kernel_size=(3, 3), activation=’relu’))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation=’relu’))
model.add(Dropout(0.5))
model.add(Dense(10, activation=’softmax’))

model.compile(loss=’categorical_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’])

# 训练模型
model.fit(x_train, y_train, batch_size=128, epochs=10, verbose=1, validation_data=(x_test, y_test))

# 评估模型
score = model.evaluate(x_test, y_test, verbose=0)
print(‘Test loss:’, score[0])
print(‘Test accuracy:’, score[1])

# 使用模型进行预测
def predict_digit(image):
image = image.reshape(1, 28, 28, 1)
image = image.astype(‘float32’)
image /= 255

prediction = model.predict(image)
digit = np.argmax(prediction)

return digit

# 加载一张手写数字图片进行测试
from PIL import Image

image_path = ‘path_to_your_image.jpg’
image = Image.open(image_path).convert(‘L’)
image = image.resize((28, 28))
image = np.array(image)

predicted_digit = predict_digit(image)
print(‘Predicted digit:’, predicted_digit)
“`

你需要安装Keras、TensorFlow和PIL库来运行这个程序。你可以将手写数字图片的路径替换为你自己的图片路径，然后运行程序即可识别图片中的手写数字，并输出预测结果。