手写体文字识别优惠卷

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

# 构建简单的CNN模型
model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dense(64, activation='relu'),
    Dense(10, activation='softmax')
])

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

# 假设我们已经有训练好的模型权重
model.load_weights('handwritten_text_recognition_model.h5')

# 加载并预处理图像
import numpy as np
from PIL import Image

def preprocess_image(image_path):
    img = Image.open(image_path).convert('L')  # 转换为灰度图像
    img = img.resize((28, 28))  # 调整大小以匹配模型输入
    img_array = np.array(img) / 255.0  # 归一化
    return np.expand_dims(img_array, axis=-1)  # 增加通道维度

# 测试图像识别
test_image = preprocess_image('test_coupon.png')
prediction = model.predict(test_image)
print(f"识别结果: {np.argmax(prediction)}")