深度学习 车牌识别

import tensorflow as tf
from tensorflow.keras import layers, models

# 构建卷积神经网络模型
def create_model(input_shape):
    model = models.Sequential([
        layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
        layers.MaxPooling2D((2, 2)),
        layers.Conv2D(64, (3, 3), activation='relu'),
        layers.MaxPooling2D((2, 2)),
        layers.Conv2D(128, (3, 3), activation='relu'),
        layers.Flatten(),
        layers.Dense(128, activation='relu'),
        layers.Dropout(0.5),
        layers.Dense(7, activation='softmax')  # 假设车牌号码由7个字符组成
    ])
    return model

# 假设输入图像尺寸为128x64像素，单通道灰度图
input_shape = (64, 128, 1)
model = create_model(input_shape)

# 编译模型
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# 打印模型概述
model.summary()