本文介绍利用MNIST数据集构建多数字识别模型的过程。先通过预处理MNIST数据，拼接生成含多个数字的训练集和测试集；接着安装PaddleOCR及依赖，下载预训练模型；然后训练模型并导出；最后采样测试图片，用导出的模型进行识别测试。

引入

传统的基于 MNIST 数据集的手写数字识别模型只能识别单个数字但实际使用环境中，多数字识别才是更加常见的情况本次就使用 MNIST 数据集，通过拼接数据的方式，实现多数字识别模型

构建数据集

拼接采样数据集In [ ]

%cd ~!mkdir dataset !mkdir dataset/train!mkdir dataset/testimport cv2import randomimport numpy as npfrom tqdm import tqdmfrom paddle.vision.datasets import MNIST# 加载数据集mnist_train = MNIST(mode='train', backend='cv2')mnist_test = MNIST(mode='test', backend='cv2')# 数据集预处理datas_train = {}for i in range(len(mnist_train)):    sample = mnist_train[i]    x, y = sample[0], sample[1]    _sum = np.sum(x, axis=0)    _where = np.where(_sum > 0)    x = 255 - x[:, _where[0][0]: _where[0][-1]+1]    if str(y[0]) in datas_train:        datas_train[str(y[0])].append(x)    else:        datas_train[str(y[0])] = [x]datas_test = {}for i in range(len(mnist_test)):    sample = mnist_test[i]    x, y = sample[0], sample[1]    _sum = np.sum(x, axis=0)    _where = np.where(_sum > 0)    x = 255 - x[:, _where[0][0]: _where[0][-1]+1]    if str(y[0]) in datas_test:        datas_test[str(y[0])].append(x)    else:        datas_test[str(y[0])] = [x]# 图片拼接采样datas_train_list = []for num in tqdm(range(0, 999)):    for _ in range(1000):        imgs = [255 - np.zeros((28, np.random.randint(10)))]        for word in str(num):            index = np.random.randint(0, len(datas_train[word]))            imgs.append(datas_train[word][index])            imgs.append(255 - np.zeros((28, np.random.randint(10))))        img = np.concatenate(imgs, 1)        cv2.imwrite('dataset/train/%03d_%04d.webp' % (num, _), img)        datas_train_list.append('train/%03d_%04d.webp\t%d\n' % (num, _, num))datas_test_list = []for num in tqdm(range(0, 999)):    for _ in range(50):        imgs = [255 - np.zeros((28, np.random.randint(10)))]        for word in str(num):            index = np.random.randint(0, len(datas_test[word]))            imgs.append(datas_test[word][index])            imgs.append(255 - np.zeros((28, np.random.randint(10))))        img = np.concatenate(imgs, 1)        cv2.imwrite('dataset/test/%03d_%04d.webp' % (num, _), img)        datas_test_list.append('test/%03d_%04d.webp\t%d\n' % (num, _, num))# 数据列表生成with open('dataset/train.txt', 'w') as f:    for line in datas_train_list:        f.write(line)with open('dataset/test.txt', 'w') as f:    for line in datas_test_list:        f.write(line)

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数据样例展示

       

安装 PaddleOCR

In [ ]

!git clone https://gitee.com/PaddlePaddle/PaddleOCR -b release/2.1 --depth 1

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安装依赖环境

In [ ]

!pip install imgaug pyclipper lmdb Levenshtein

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下载预训练模型

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%cd ~/PaddleOCR!wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar!cd pretrain_models && tar -xf ch_ppocr_mobile_v2.0_rec_pre.tar && rm -rf ch_ppocr_mobile_v2.0_rec_pre.tar

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模型训练

In [8]

%cd ~/PaddleOCR!python tools/train.py -c ../multi_mnist.yml

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模型导出

In [34]

%cd ~/PaddleOCR!python3 tools/export_model.py \    -c ../multi_mnist.yml -o Global.pretrained_model=../output/multi_mnist/best_accuracy \    Global.load_static_weights=False \    Global.save_inference_dir=../inference/multi_mnist

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采样测试图片

In [45]

%cd ~/PaddleOCR!mkdir ~/test_imgsimport cv2import randomimport numpy as npfrom tqdm import tqdmfrom paddle.vision.datasets import MNIST# 加载数据集mnist_test = MNIST(mode='test', backend='cv2')# 数据集预处理datas_test = {}for i in range(len(mnist_test)):    sample = mnist_test[i]    x, y = sample[0], sample[1]    _sum = np.sum(x, axis=0)    _where = np.where(_sum > 0)    x = 255 - x[:, _where[0][0]: _where[0][-1]+1]    if str(y[0]) in datas_test:        datas_test[str(y[0])].append(x)    else:        datas_test[str(y[0])] = [x]# 图片拼接采样for num in range(0, 1000):    imgs = [255 - np.zeros((28, np.random.randint(10)))]    for word in str(num):        index = np.random.randint(0, len(datas_test[word]))        imgs.append(datas_test[word][index])        imgs.append(255 - np.zeros((28, np.random.randint(10))))    img = np.concatenate(imgs, 1)    cv2.imwrite('../test_imgs/%03d.webp' % num , img)

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模型测试

In [46]

%cd ~/PaddleOCR!python tools/infer/predict_rec.py \    --image_dir="../test_imgs" \    --rec_model_dir="../inference/multi_mnist/" \    --rec_image_shape="3, 28, 64" \    --rec_char_type="ch" \    --rec_char_dict_path="../label_list.txt"

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