【OCR】【专题系列】四、基于RCNN-CTC的不定长文本识别

【OCR】【专题系列】四、基于RCNN-CTC的不定长文本识别

目录

一、论文阅读

二、代码实现

三、结果讨论

一、论文阅读

        在上篇博客《【OCR】基于图像分类的定长文本识别》中，通过图像像素分类的方法实现固定图片的识别方法。本篇主要是针对OCR经典论文《An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition》代码复现和实验结果分析。
        论文的网络结构如下图所示：

 图1 CRNN-CTC网络结构图

         网络结构主要包括CNN和BiLSTM两部分构成，CNN主要用于图像特征信息提取，BiLSTM连接语义信息，最后通过CTCLoss损失用于约束不定长文本连续的错误识别。在开源代码的基础上，本文针对自己已有数据集复现了代码、做了小规模实验，局部测试了模型效果。

二、代码实现

        本文代码结构承接上文，模型结构通过Model类完成，数据通过MyDataset类+collate_fn完成，相关配置通过configs完成配置。在模型定义中通过pytorch实现CRNN-CTC的模型，损失函数采用torch.nn.ctcloss，所用词表可通过字符串按顺序构建。下述为代码实现，修改对应配置项即可跑通复现实验。

from torch.utils.data import Dataset
from torch import nn as nn
import torchvision.transforms as T
import torch.nn.functional as F
from torch.utils.data import DataLoader
import os
import torch
from PIL import Image
from tqdm import tqdm
import numpy as np
 
 
class configs():
    def __init__(self):
        #Data
        self.data_dir = './captcha_datasets'
        self.train_dir = 'train-data'
        self.valid_dir = 'valid-data'
        self.test_dir = 'test-data-1'
        self.save_model_dir = 'models_ocr'
        self.get_lexicon_dir = './lbl2id_map.txt'
        self.img_transform = T.Compose([
            T.Resize((32, 100)),
            T.ToTensor(),
            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 
        ])
        # self.lexicon = self.get_lexicon(lexicon_name=self.get_lexicon_dir)
        self.lexicon = "0123456789"+"_"
        self.all_chars = {v: k for k, v in enumerate(self.lexicon)}
        self.all_nums = {v: k for v, k in enumerate(self.lexicon)}
        self.class_num = len(self.lexicon)
        self.label_word_length = 4
 
        #train
        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
        self.batch_size = 64
        self.epoch = 31
        self.save_model_fre_epoch = 1
        self.nh = 128 # 隐层数量
 
        self.istrain = True
        self.istest = True
 
    def get_lexicon(self,lexicon_name):
        '''
        #获取词表 lbl2id_map.txt'，词表格式如下
        #0\t0\n
        #a\t1\n
        #...
        #z\t63\n
        :param lexicons_name:
        :return:
        '''
        lexicons = open(lexicon_name, 'r', encoding='utf-8').readlines()
        lexicons_str = ''.join(word[0].split('\t')[0] for word in lexicons)
        return lexicons_str
 
cfg = configs()
 
#model define
class BidirectionalLSTM(nn.Module):
 
    def __init__(self, nIn, nHidden, nOut):
        super(BidirectionalLSTM, self).__init__()
 
        self.rnn = nn.LSTM(nIn, nHidden, bidirectional=True)
        self.embedding = nn.Linear(nHidden * 2, nOut)
 
    def forward(self, input):
        recurrent, _ = self.rnn(input)
        T, b, h = recurrent.size()
        t_rec = recurrent.view(T * b, h)
 
        output = self.embedding(t_rec)  # [T * b, nOut]
        output = output.view(T, b, -1)
 
        return output
 
class Model(nn.Module):
    def __init__(self, imgH, nc, nclass, nh, n_rnn=2, leakyRelu=False):
        super(Model, self).__init__()
        assert imgH % 16 == 0, 'imgH has to be a multiple of 16'
 
        ks = [3, 3, 3, 3, 3, 3, 2]
        ps = [1, 1, 1, 1, 1, 1, 0]
        ss = [1, 1, 1, 1, 1, 1, 1]
        nm = [64, 128, 256, 256, 512, 512, 512]
 
        cnn = nn.Sequential()
 
        def convRelu(i, batchNormalization=False):
            nIn = nc if i == 0 else nm[i - 1]
            nOut = nm[i]
            cnn.add_module('conv{0}'.format(i),
                           nn.Conv2d(nIn, nOut, ks[i], ss[i], ps[i]))
            if batchNormalization:
                cnn.add_module('batchnorm{0}'.format(i), nn.BatchNorm2d(nOut))
            if leakyRelu:
                cnn.add_module('relu{0}'.format(i),
                               nn.LeakyReLU(0.2, inplace=True))
            else:
                cnn.add_module('relu{0}'.format(i), nn.ReLU(True))
 
        convRelu(0)
        cnn.add_module('pooling{0}'.format(0), nn.MaxPool2d(2, 2))  # 64x16x64
        convRelu(1)
        cnn.add_module('pooling{0}'.format(1), nn.MaxPool2d(2, 2))  # 128x8x32
        convRelu(2, True)
        convRelu(3)
        cnn.add_module('pooling{0}'.format(2),
                       nn.MaxPool2d((2, 2), (2, 1), (0, 1)))  # 256x4x16
        convRelu(4, True)
        convRelu(5)
        cnn.add_module('pooling{0}'.format(3),
                       nn.MaxPool2d((2, 2), (2, 1), (0, 1)))  # 512x2x16
        convRelu(6, True)  # 512x1x16
 
        self.cnn = cnn
        self.rnn = nn.Sequential(
            BidirectionalLSTM(512, nh, nh),
            BidirectionalLSTM(nh, nh, nclass))
 
    def forward(self, input):
        # conv features
        conv = self.cnn(input)
        b, c, h, w = conv.size()
        assert h == 1, "the height of conv must be 1"
        conv = conv.squeeze(2)
        conv = conv.permute(2, 0, 1)  # [w, b, c]
 
        # rnn features
        output = self.rnn(conv)
 
        # add log_softmax to converge output
        output = F.log_softmax(output, dim=2)
        output_lengths = torch.full(size=(output.size(1),), fill_value=output.size(0), dtype=torch.long,
                                    device=cfg.device)
 
        return output, output_lengths
 
    def backward_hook(self, module, grad_input, grad_output):
        for g in grad_input:
            g[g != g] = 0  # replace all nan/inf in gradients to zero
 
#dataset define
class MyDataset(Dataset):
 
    def __init__(self, path: str, transform=None, ):
        if transform == None:
            self.transform = T.Compose(
                [
                    T.ToTensor()
                ])
        else:
            self.transform = transform
        self.path = path
        self.picture_list = list(os.walk(self.path))[0][-1]
 
    def __len__(self):
        return len(self.picture_list)
 
    def __getitem__(self, item):
        """
        :param item: ID
        :return:  (图片，标签)
        """
        picture_path_list = self._load_picture()
        img = Image.open(picture_path_list[item]).convert("RGB")  
        img = self.transform(img)
        label = os.path.splitext(self.picture_list[item])[0].split("_")[1]
 
        label = [[cfg.all_chars[i]] for i in label]
        label = torch.as_tensor(label, dtype=torch.int64)
 
        return img, label
 
    def _load_picture(self):
        return [self.path + '/' + i for i in self.picture_list]
 
def collate_fn(batch):
    sequence_lengths = []
    max_width, max_height = 0, 0
    for image, label in batch:
        if image.size(1) > max_height:
            max_height = image.size(1)
        if image.size(2) > max_width:
            max_width = image.size(2)
        sequence_lengths.append(label.size(0))
    seq_lengths = torch.LongTensor(sequence_lengths)
    seq_tensor = torch.zeros(seq_lengths.size(0), seq_lengths.max()).long()
    img_tensor = torch.zeros(seq_lengths.size(0), 3, max_height, max_width)
    for idx, (image, label) in enumerate(batch):
        seq_tensor[idx, :label.size(0)] = torch.squeeze(label)
        img_tensor[idx, :, :image.size(1), :image.size(2)] = image
    return img_tensor, seq_tensor, seq_lengths
 
class ocr():
    def train(self):
        model = Model(imgH = 32,nc = 3, nclass = cfg.class_num, nh = cfg.nh)
        model = model.to(cfg.device)
        criterion = torch.nn.CTCLoss(blank=cfg.class_num - 1, zero_infinity=True)
        optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
        model.train()
 
        # train dataset
        train_dataset = MyDataset(os.path.join(cfg.data_dir, cfg.train_dir),
                                  transform=cfg.img_transform)  # 训练路径以及transform
        train_loader = DataLoader(dataset=train_dataset, batch_size=cfg.batch_size, shuffle=True,drop_last=True,num_workers=0, collate_fn=collate_fn)
 
        for epoch in range(cfg.epoch):
            bar = tqdm(enumerate(train_loader,0))
            loss_sum = []
            total = 0
            correct = 0
            for idx, (images, labels,label_lengths) in bar:
                images, labels, label_lengths = images.to(cfg.device), \
                                                labels.to(cfg.device), \
                                                label_lengths.to(cfg.device)
                optimizer.zero_grad()
                outputs, output_lengths = model(images)
                loss = criterion(outputs, labels, output_lengths, label_lengths)
                loss.backward()
                optimizer.step()
                loss_sum.append(loss.item())
                c, t = self.calculat_train_acc(outputs, labels, label_lengths)
                correct +=c
                total += t
                bar.set_description("epcoh:{} idx:{},loss:{:.6f},acc:{:.6f}".format(epoch, idx, np.mean(loss_sum),100 * correct / total))
            if epoch%cfg.save_model_fre_epoch ==0:
                torch.save(model.state_dict(), os.path.join(cfg.save_model_dir,"epoch_"+str(epoch)+'.pkl'), _use_new_zipfile_serialization=True)  # 模型保存
                torch.save(optimizer.state_dict(), os.path.join(cfg.save_model_dir,"epoch_"+str(epoch)+"_opti"+'.pkl'), _use_new_zipfile_serialization=True)  # 优化器保存
 
    def infer(self):
        for modelname in os.listdir(cfg.save_model_dir):
            #model define
            train_weights_path = os.path.join(cfg.save_model_dir, modelname)
            train_weights_dict = torch.load(train_weights_path)
            model = Model(imgH=32, nc=3, nclass=cfg.class_num, nh=cfg.nh)
            model.load_state_dict(train_weights_dict, strict=True)
            model = model.to(cfg.device)
            model.eval()
 
            #test dataset
            test_dataset = MyDataset(os.path.join(cfg.data_dir, cfg.test_dir), transform=cfg.img_transform)  # 训练路径以及transform
            test_loader = DataLoader(dataset=test_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn)
 
            total = 0
            correct = 0
            results = []
            for idx,(images, labels,label_lengths) in enumerate(test_loader,0):
                labels = torch.squeeze(labels).to(cfg.device)
 
                with torch.no_grad():
                    predicts,output_lengths = model(images.to(cfg.device))
                    c, t, result = self.calculat_infer_acc(predicts, labels, label_lengths)
                    correct += c
                    total += t
                    results.append(result)
            print("model name: "+modelname+'\t'+"|| acc: "+str(correct / total)+'\n')
 
    # 计算训练准确率
    def calculat_train_acc(self,output, target, target_lengths):
        output = torch.argmax(output, dim=-1)
        output = output.permute(1, 0)
 
        correct_num = 0
        for predict, label, label_length in zip(output, target, target_lengths):
            predict = torch.unique_consecutive(predict)
            predict = predict[predict != (cfg.class_num - 1)]
            if (predict.size()[0] == label_length.item()
                    and (predict == label[:label_length.item()]).all()):
                correct_num += 1
        return correct_num, target.size(0)
 
    #计算推理准确率
    def calculat_infer_acc(self,output, target, target_lengths):
        output = torch.argmax(output, dim=-1)
        output = output.permute(1, 0)
 
        correct_num = 0
        total_num = 0
        predict_list = []
        for predict, label, label_length in zip(output, target, target_lengths):
            total_num +=1
            predict = torch.unique_consecutive(predict)
            predict = predict[predict != (cfg.class_num - 1)]
            predict_list = predict.cpu().tolist()
            label_list = target.cpu().tolist()
            if predict_list == label_list:
                correct_num += 1
 
        if predict_list == []:
            predict_str = '____'
        else:
            predict_str = ''.join([cfg.all_nums[s] for s in predict_list])
        label_str = ''.join([cfg.all_nums[s] for s in label_list])
        return correct_num, total_num,','.join([predict_str,label_str])
 
if __name__ == '__main__':
    myocr = ocr()
    if cfg.istrain == True:
        myocr.train()
    if cfg.istest == True:
        myocr.infer()

三、结果讨论

        本文采用captcha_datasets数据集作为实验数据集，训练集：验证集：测试集=25000:10000:10000，图片内容主要是数字验证码。在本次实验中采用30次迭代测试模型效果，train-ctcloss、train-acc、test-acc效果如下表所示

        部分识别效果图展示：

 图 识别效果实例图

        由上述的训练过程可以看出，ctcloss在5次迭代后就有了较好的识别效果。原因是数据量较小、数据质量较单一，可以期待在更大数据集上的识别效果。