text: import torch
  from torch.autograd import Variable
  import utils
  import dataset
  from PIL import Image
  from dataset2 import RawDataset, AlignCollate
  import models.crnn as crnn
  
  
  model_path = './data/crnn.pth'
  img_path = RawDataset(root='./data')
  alphabet = '0123456789abcdefghijklmnopqrstuvwxyz'
  
  model = crnn.CRNN(32, 1, 37, 256)
  if torch.cuda.is_available():
      model = model.cuda()
  model.load_state_dict(torch.load(model_path))
  
  converter = utils.strLabelConverter(alphabet)
  
  transformer = dataset.resizeNormalize((100, 32))
  image = Image.open(img_path).convert('L')
  image = transformer(image)
  if torch.cuda.is_available():
      image = image.cuda()
  image = image.view(1, *image.size())
  image = Variable(image)
  
  model.eval()
  preds = model(image)
  
  _, preds = preds.max(2)
  preds = preds.transpose(1, 0).contiguous().view(-1)
  
  preds_size = Variable(torch.IntTensor([preds.size(0)]))
  raw_pred = converter.decode(preds.data, preds_size.data, raw=True)
  sim_pred = converter.decode(preds.data, preds_size.data, raw=False)
  print('%-20s' % (sim_pred))
  