diff --git a/inference.py b/inference.py
new file mode 100644
index 0000000..d3bff4d
--- /dev/null
+++ b/inference.py
@@ -0,0 +1,100 @@
+import cv2
+import torch
+from PIL import Image
+import matplotlib.pyplot as plt
+from collections import OrderedDict
+from torch.autograd import Variable
+
+import tools.utils as utils
+import tools.dataset as dataset
+from models.moran import MORAN
+
+
+class Recognizer:
+    def __init__(self, model_path):
+        alphabet = '0:1:2:3:4:5:6:7:8:9:a:b:c:d:e:f:g:h:i:j:k:l:m:n:o:p:q:r:s:t:u:v:w:x:y:z:$'
+
+        self.cuda_flag = torch.cuda.is_available()
+        if self.cuda_flag:
+            self.MORAN = MORAN(1, len(alphabet.split(':')), 256, 32, 100, BidirDecoder=True, CUDA=self.cuda_flag)
+            self.MORAN = self.MORAN.cuda()
+        else:
+            self.MORAN = MORAN(1, len(alphabet.split(':')), 256, 32, 100, BidirDecoder=True, inputDataType='torch.FloatTensor', CUDA=self.cuda_flag)
+
+        print('loading pretrained model from %s' % model_path)
+        if self.cuda_flag:
+            state_dict = torch.load(model_path)
+        else:
+            state_dict = torch.load(model_path, map_location='cpu')
+
+        MORAN_state_dict_rename = OrderedDict()
+        for k, v in state_dict.items():
+            name = k.replace("module.", "") # remove `module.`
+            MORAN_state_dict_rename[name] = v
+        self.MORAN.load_state_dict(MORAN_state_dict_rename)
+
+        for p in self.MORAN.parameters():
+            p.requires_grad = False
+        self.MORAN.eval()
+
+        self.converter = utils.strLabelConverterForAttention(alphabet, ':')
+        self.transformer = dataset.resizeNormalize((100, 32))
+
+    def preprocess(self, img):
+        image = Image.fromarray(img[..., ::-1]).convert('L')
+        image = self.transformer(image)
+        image = image.view(1, *image.size())
+        return image
+
+    def predict(self, img_batch):
+        batch_size = int(img_batch.size(0))
+
+        if self.cuda_flag:
+            img_batch = img_batch.cuda()
+        # img_batch = Variable(img_batch)
+
+        text = torch.LongTensor(batch_size * 5)
+        length = torch.IntTensor(batch_size)
+        # text = Variable(text)
+        # length = Variable(length)
+
+        max_iter = 20
+        t, l = self.converter.encode(['0' * max_iter] * batch_size)
+        utils.loadData(text, t)
+        utils.loadData(length, l)
+
+        output = self.MORAN(img_batch, length, text, text, test=True, debug=True)
+        return output, length
+
+    def post_process(self, output, length):
+        preds, preds_reverse = output[0]
+        # demo = output[1]
+
+        _, preds = preds.max(1)
+        _, preds_reverse = preds_reverse.max(1)
+
+        sim_preds = self.converter.decode(preds.data, length.data)
+        sim_preds = list(map(lambda x: x.strip().split('$')[0], sim_preds))
+        sim_preds_reverse = self.converter.decode(preds_reverse.data, length.data)
+        sim_preds_reverse = list(map(lambda x: x.strip().split('$')[0], sim_preds_reverse))
+        return sim_preds, sim_preds_reverse
+
+    def __call__(self, images):
+        unit_size = len(images) == 1
+        if unit_size:
+            images = images * 2
+
+        img_tensors = []
+        for img in images:
+            img_tensors.append(self.preprocess(img))
+
+        img_batch = torch.cat(img_tensors)
+
+        output, length = self.predict(img_batch)
+
+        sim_preds, sim_preds_reverse = self.post_process(output, length)
+
+        if unit_size:
+            sim_preds = sim_preds[:1]
+
+        return sim_preds
\ No newline at end of file