我正在学习对神经网络和其他网络进行编程，我想知道如何获得图像中的数字，例如，如果我传递一个写有 123 的图像，用我的模型得到有 123 个写，我尝试使用PyTesseract不是很精确，我想用神经网络来做，​​我当前的代码很简单，它可以识别mnist数据集的数字，例如：

import tensorflow as tf
from tensorflow.keras import Sequential, optimizers
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
import matplotlib.pyplot as plt

mnist = tf.keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

print('train_images.shape:', train_images.shape)
print('test_images.shape:', test_images.shape)
plt.imshow(train_images[0])

train_images = train_images.reshape((60000, 28, 28, 1))
test_images = test_images.reshape((10000, 28, 28, 1))

train_images = train_images.astype('float32') / 255
test_images = test_images.astype('float32') / 255

train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
 

model = Sequential()

model.add(Conv2D(32, (5, 5), activation = 'relu', input_shape = (28, 28, 1)))
model.add(MaxPooling2D((2, 2)))

model.add(Conv2D(64, (5, 5), activation = 'relu'))
model.add(MaxPooling2D((2, 2)))

model.add(Flatten())

model.add(Dense(10, activation = 'softmax'))

model.summary()

model.compile(loss = 'categorical_crossentropy', optimizer = 'sgd', metrics = ['accuracy'])

model.fit(train_images, train_labels, batch_size = 100, epochs = 5, verbose = 1)

test_loss, test_accuracy = model.evaluate(test_images, test_labels)

print('Test accuracy:', test_accuracy)

但是我需要知道如何将带有数字序列的图像传递给它，并且它可以识别有问题的数字，有人知道我该怎么做吗？