diff --git a/detector/ctpn/infer.py b/detector/ctpn/infer.py
index 0f70dbb..eaaf34a 100644
--- a/detector/ctpn/infer.py
+++ b/detector/ctpn/infer.py
@@ -342,8 +342,12 @@ def random_test(net):
 
 if __name__ == '__main__':
     running_mode = sys.argv[2]  # cpu or gpu
+    print("Mode: %s" % running_mode)
     net = Net.CTPN()
-    net.load_state_dict(torch.load(MODEL, map_location=running_mode))
+    if running_mode == 'cpu':
+        net.load_state_dict(torch.load(MODEL, map_location=running_mode))
+    else:
+        net.load_state_dict(torch.load(MODEL))
     print(net)
     net.eval()
 
diff --git a/recognizer/crnn/Config.py b/recognizer/crnn/Config.py
index f4d1091..ce03e2b 100644
--- a/recognizer/crnn/Config.py
+++ b/recognizer/crnn/Config.py
@@ -8,7 +8,7 @@
 gpu_id = '4'
 model_dir = './model'
 data_worker = 4
-batch_size = 128
+batch_size = 64
 img_height = 32
 img_width = 100
 alphabet = alphabets.alphabet
diff --git a/recognizer/crnn/alphabets.py b/recognizer/crnn/alphabets.py
new file mode 100644
index 0000000..7ddc43b
--- /dev/null
+++ b/recognizer/crnn/alphabets.py
@@ -0,0 +1,2 @@
+#coding=utf-8
+alphabet = 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diff --git a/recognizer/crnn/infer.py b/recognizer/crnn/infer.py
index df47699..f422ac9 100644
--- a/recognizer/crnn/infer.py
+++ b/recognizer/crnn/infer.py
@@ -8,12 +8,27 @@
 from PIL import Image
 import Net.net as Net
 import alphabets
+import numpy as np
+import cv2
 
-crnn_model_path = 'trained_models/mixed_second_finetune_acc97p7.pth'
-IMG_ROOT = './test'
+crnn_model_path = './model/mixed_second_finetune_acc97p7.pth'
+IMG_ROOT = './test_images'
+running_mode = 'gpu'
 alphabet = alphabets.alphabet
 nclass = len(alphabet) + 1
 
+# Testing images are scaled to have height 32. Widths are
+# proportionally scaled with heights, but at least 100 pixels
+def scale_img_para(img, min_width=100, fixed_height=32):
+    height = img.size[1]
+    width = img.size[0]
+    scale = float(fixed_height)/height
+    w = int(width * scale)
+    if w < min_width:
+        w = min_width
+
+    return w, fixed_height
+
 
 def crnn_recognition(cropped_image, model):
     converter = lib.utils.strLabelConverter(alphabet)
@@ -21,8 +36,9 @@ def crnn_recognition(cropped_image, model):
     image = cropped_image.convert('L')
 
     ##
-    w = int(image.size[0] / (280 * 1.0 / 160))
-    transformer = lib.dataset.resizeNormalize((w, 32))
+    #w = int(image.size[0] / (280 * 1.0 / 160))
+    w, h = scale_img_para(image)
+    transformer = lib.dataset.resizeNormalize((w, h))
     image = transformer(image)
     if torch.cuda.is_available():
         image = image.cuda()
@@ -44,11 +60,13 @@ def crnn_recognition(cropped_image, model):
 
     # crnn network
     model = Net.CRNN(nclass)
-    if torch.cuda.is_available():
+    if running_mode == 'gpu' and torch.cuda.is_available():
         model = model.cuda()
-    print('loading pretrained model from {0}'.format(crnn_model_path))
+        model.load_state_dict(torch.load(crnn_model_path))
+    else:
+        model.load_state_dict(torch.load(crnn_model_path, map_location='cpu'))
 
-    model.load_state_dict(torch.load(crnn_model_path))
+    print('loading pretrained model from {0}'.format(crnn_model_path))
 
     files = os.listdir(IMG_ROOT)
     for file in files: