diff --git a/.gitignore b/.gitignore
index ff10645..f0dff23 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,3 +7,4 @@ Prj-Linux/lpr/TEST_*
 Prj-Linux/*build*/
 *.pyc
 /Prj-PHP/build
+*.idea*
diff --git a/hyperlpr_pip_pkg/demo.py b/hyperlpr_pip_pkg/demo.py
index 3b5fd94..862d248 100644
--- a/hyperlpr_pip_pkg/demo.py
+++ b/hyperlpr_pip_pkg/demo.py
@@ -1,4 +1,4 @@
 from hyperlpr import *
 import cv2
-image = cv2.imread("test.png")
-print(HyperLPR_plate_recognition(image,16,charSelectionDeskew=False))
+image = cv2.imread("./test_images/test_db2.jpg")
+print(HyperLPR_plate_recognition(image,16,charSelectionDeskew=False,DB=True))
diff --git a/hyperlpr_pip_pkg/hyperlpr/__init__.py b/hyperlpr_pip_pkg/hyperlpr/__init__.py
index 0b68157..e999c48 100644
--- a/hyperlpr_pip_pkg/hyperlpr/__init__.py
+++ b/hyperlpr_pip_pkg/hyperlpr/__init__.py
@@ -4,5 +4,5 @@ from .hyperlpr import LPR
 import os
 
 PR = LPR(os.path.join(os.path.split(os.path.realpath(__file__))[0],"models"))
-def HyperLPR_plate_recognition(Input_BGR,minSize=30,charSelectionDeskew=True , region = "CH"):
-    return PR.plate_recognition(Input_BGR,minSize,charSelectionDeskew)
+def HyperLPR_plate_recognition(Input_BGR,minSize=30,charSelectionDeskew=True , region = "CH",DB=True):
+    return PR.plate_recognition(Input_BGR,minSize,charSelectionDeskew,DB)
diff --git a/hyperlpr_pip_pkg/hyperlpr/hyperlpr.py b/hyperlpr_pip_pkg/hyperlpr/hyperlpr.py
index 7269156..5ff0970 100644
--- a/hyperlpr_pip_pkg/hyperlpr/hyperlpr.py
+++ b/hyperlpr_pip_pkg/hyperlpr/hyperlpr.py
@@ -19,12 +19,14 @@ class LPR():
 
         charLocPath= os.path.join(folder,"cascade/char/char_single.xml")
         detectorPath = os.path.join(folder,"cascade/detector/detector_ch.xml")
+        detectorPathDB = os.path.join(folder,"cascade/detector/cascade_double.xml")
         modelRecognitionPath = [os.path.join(folder,"dnn/SegmenationFree-Inception.prototxt"),os.path.join(folder,"dnn/SegmenationFree-Inception.caffemodel")]
         modelFineMappingPath= [os.path.join(folder,"dnn/HorizonalFinemapping.prototxt"),os.path.join(folder,"dnn/HorizonalFinemapping.caffemodel")]
         mini_ssd_path= [os.path.join(folder,"dnn/mininet_ssd_v1.prototxt"),os.path.join(folder,"dnn/mininet_ssd_v1.caffemodel")]
         refine_net_path = [os.path.join(folder,"dnn/refinenet.prototxt"),os.path.join(folder,"dnn/refinenet.caffemodel")]
 
         self.detector = cv2.CascadeClassifier(detectorPath)
+        self.detectorDB = cv2.CascadeClassifier(detectorPathDB)
         self.charLoc = cv2.CascadeClassifier(charLocPath)
         self.modelRecognition = cv2.dnn.readNetFromCaffe(*modelRecognitionPath)
         self.ssd_detection = cv2.dnn.readNetFromCaffe(*mini_ssd_path)
@@ -64,7 +66,7 @@ class LPR():
                 cropped_images.append([cropped ,[x1,y1,x2,y2]])
         return cropped_images
 
-    def detect_traditional(self,image_gray,resize_h = 720,en_scale =1.1,minSize = 30):
+    def detect_traditional(self,image_gray,resize_h = 720,en_scale =1.1,minSize = 30,DB=True):
         """
         Detect the approximate location of plate via opencv build-in cascade detection.
         :param image_gray:  input single channel image (gray) .
@@ -73,7 +75,10 @@ class LPR():
         :param minSize:  minSize of plate increase this parameter can increase the speed of detection.
         :return:   the results.
         """
-        watches = self.detector.detectMultiScale(image_gray, en_scale, 3, minSize=(minSize*4, minSize))
+        if DB:
+            watches = self.detectorDB.detectMultiScale(image_gray, en_scale, 3, minSize=(minSize*4, minSize))
+        else:
+            watches = self.detector.detectMultiScale(image_gray, en_scale, 3, minSize=(minSize*4, minSize))
         cropped_images = []
         for (x, y, w, h) in watches:
             x -= w * 0.14
@@ -283,7 +288,7 @@ class LPR():
         return self.decode_ctc(y_pred)
 
 
-    def plate_recognition(self,image,minSize=30,charSelectionDeskew=True,mode='ssd'):
+    def plate_recognition(self,image,minSize=30,charSelectionDeskew=True,DB = True, mode='ssd'):
         """
         the simple pipline consists of detection . deskew , fine mapping alignment, recognition.
         :param image: the input BGR image from imread used by opencv
@@ -301,14 +306,29 @@ class LPR():
             image  = cv2.imread("tests/image")
             print(pr.plateRecognition(image))
         """
-
-        images = self.detect_ssd(image)
+        if DB:
+            image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
+            images = self.detect_traditional(image_gray)
+        else:
+            images = self.detect_ssd(image)
         res_set = []
         for j,plate in enumerate(images):
             plate,[left,top,right,bottom] = plate
-            print(left,top,right,bottom)
-            cropped = self.loose_crop(image, [left, top, right, bottom], 120 / 48)
-            cropped_finetuned = self.finetune(cropped)
+            print(left, top, right, bottom)
+            if DB:
+                w, h = right - left, bottom - top
+                plate = image[top:bottom,left:right,:]
+                crop_up = plate[int(h * 0.05):int((h) * 0.4), int(w * 0.2):int(w * 0.75)]
+                crop_down = plate[int((h) * 0.4):int(h), int(w * 0.05):w]
+                crop_up = cv2.resize(crop_up, (64, 40))
+                crop_down = cv2.resize(crop_down, (96, 40))
+                cropped_finetuned = np.concatenate([crop_up, crop_down], 1)
+                # cv2.imshow("crop",plate)
+                # cv2.waitKey(0)
+            else:
+
+                cropped = self.loose_crop(image, [left, top, right, bottom], 120 / 48)
+                cropped_finetuned = self.finetune(cropped)
             res, confidence = self.segmentation_free_recognition(cropped_finetuned)
             res_set.append([res,confidence,[left,top,right,bottom ]])
         return res_set
diff --git a/hyperlpr_pip_pkg/hyperlpr/models/cascade/detector/cascade_double.xml b/hyperlpr_pip_pkg/hyperlpr/models/cascade/detector/cascade_double.xml
new file mode 100644
index 0000000..aacc045
--- /dev/null
+++ b/hyperlpr_pip_pkg/hyperlpr/models/cascade/detector/cascade_double.xml
@@ -0,0 +1,504 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>LBP</featureType>
+  <height>24</height>
+  <width>44</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9599999189376831e-01</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>28</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>256</maxCatCount>
+    <featSize>1</featSize></featureParams>
+  <stageNum>11</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-1.1049392223358154e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 17 -3334 -513 -573452048 -1057 -1 -267777 -1 -1025</internalNodes>
+          <leafValues>
+            -9.4297146797180176e-01 7.8370976448059082e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 36 -262198 -12062208 -169869377 -674954752 -7602177
+            -16781941 -67109937 -1073</internalNodes>
+          <leafValues>
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+        <_>
+          <internalNodes>
+            0 -1 9 1431697887 1431687647 -545270049 -262145 1413609965
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+          <leafValues>
+            -8.3008646965026855e-01 7.2340542078018188e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 1 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-1.0404651165008545e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 19 -524289 -136314913 1360393719 -2097153 -134219777
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+          <leafValues>
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+        <_>
+          <internalNodes>
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+            -1 -2097153</internalNodes>
+          <leafValues>
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+        <_>
+          <internalNodes>
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+          <leafValues>
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+    <!-- stage 2 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-1.1397969722747803e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 15 -1041 -8225 -546308113 -1 -33554433 -394753 -2359297
+            -1</internalNodes>
+          <leafValues>
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+        <_>
+          <internalNodes>
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+            -74453009 -1</internalNodes>
+          <leafValues>
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+        <_>
+          <internalNodes>
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+            -67110929 1996976642</internalNodes>
+          <leafValues>
+            -8.6068797111511230e-01 5.8224511146545410e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 3 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-9.8026764392852783e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 12 -1077953601 -1 -1073758273 -16387 -147457 -1
+            -1073758209 -1</internalNodes>
+          <leafValues>
+            -8.2477015256881714e-01 7.1671992540359497e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 47 -4345 -989855745 -271062524 -319815697 -134742265
+            1423962843 -134218173 -1</internalNodes>
+          <leafValues>
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+        <_>
+          <internalNodes>
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+            -1 -1048577</internalNodes>
+          <leafValues>
+            -8.3125960826873779e-01 5.8680748939514160e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 4 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-1.2986627817153931e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
+          <internalNodes>
+            0 -1 20 -1 -10241 1431688703 -1 -1 -1042 1163263999
+            2002780159</internalNodes>
+          <leafValues>
+            -8.2954949140548706e-01 5.9151750802993774e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 3 -44786212 -33726467 -268509185 -17409 -657175332
+            -2270500 -1198986500 -1148450934</internalNodes>
+          <leafValues>
+            -7.6026451587677002e-01 5.8319890499114990e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 28 -15794258 -78643282 -821817846 -552742962 -653344834
+            -91750417 -1622147105 -7340065</internalNodes>
+          <leafValues>
+            -7.6077878475189209e-01 5.5891805887222290e-01</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            -6.7643576860427856e-01 6.0950380563735962e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 5 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-1.3029408454895020e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
+          <internalNodes>
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+          <leafValues>
+            -7.7118909358978271e-01 6.3570922613143921e-01</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            -7.2490030527114868e-01 5.7298541069030762e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 18 2002780159 2136866815 -67109377 -19969 2147344383
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+          <leafValues>
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+        <_>
+          <internalNodes>
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+          <leafValues>
+            -7.9073858261108398e-01 5.0315058231353760e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 6 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-1.3164747953414917e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 41 -1 -1025 -1 -2228225 -8432299 -571189899 -139265 -1</internalNodes>
+          <leafValues>
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+        <_>
+          <internalNodes>
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+            -1049857 -277</internalNodes>
+          <leafValues>
+            -7.3563832044601440e-01 6.3897633552551270e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 14 -1565523969 -1347420193 -277086209 -1342177313
+            -134217729 -263169 -285212673 -1459618305</internalNodes>
+          <leafValues>
+            -8.1234931945800781e-01 4.9628043174743652e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 33 -1079312417 -83826176 -33686017 -570426508
+            -1627464961 -5377 -277761 -17</internalNodes>
+          <leafValues>
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+        <_>
+          <internalNodes>
+            0 -1 25 -66 -4116 1607291240 -5298489 -9847160 2011036101
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+          <leafValues>
+            -8.2066470384597778e-01 4.9184983968734741e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 7 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-1.1098697185516357e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
+          <internalNodes>
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+            -1075052545 -5</internalNodes>
+          <leafValues>
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+        <_>
+          <internalNodes>
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+          <leafValues>
+            -6.5710061788558960e-01 6.1531358957290649e-01</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
+          <internalNodes>
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+          <leafValues>
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+    <!-- stage 8 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-2.0547957420349121e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
+          <internalNodes>
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+            -138412033 3334395 -234882305</internalNodes>
+          <leafValues>
+            -6.9217604398727417e-01 5.4744583368301392e-01</leafValues></_>
+        <_>
+          <internalNodes>
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+            -572524625 -135266305 -32833</internalNodes>
+          <leafValues>
+            -7.1100026369094849e-01 5.3469187021255493e-01</leafValues></_>
+        <_>
+          <internalNodes>
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+            -687087094</internalNodes>
+          <leafValues>
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+        <_>
+          <internalNodes>
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+          <leafValues>
+            -5.8823972940444946e-01 6.1824375391006470e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 9 -->
+    <_>
+      <maxWeakCount>6</maxWeakCount>
+      <stageThreshold>-1.6300759315490723e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
+          <internalNodes>
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+          <leafValues>
+            -6.3567954301834106e-01 5.9044981002807617e-01</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
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+          <leafValues>
+            -5.7118493318557739e-01 6.4167028665542603e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 10 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-2.1370947360992432e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
+          <internalNodes>
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+            -102721404 1073685759 -1366622208</internalNodes>
+          <leafValues>
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+        <_>
+          <internalNodes>
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diff --git a/hyperlpr_pip_pkg/test_images/test.jpg b/hyperlpr_pip_pkg/test_images/test.jpg
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diff --git a/hyperlpr_pip_pkg/test_images/test_db.jpg b/hyperlpr_pip_pkg/test_images/test_db.jpg
new file mode 100644
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diff --git a/hyperlpr_pip_pkg/test_images/test_db1.jpg b/hyperlpr_pip_pkg/test_images/test_db1.jpg
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diff --git a/hyperlpr_pip_pkg/test_images/test_db2.jpg b/hyperlpr_pip_pkg/test_images/test_db2.jpg
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