diff --git a/README.md b/README.md
index 443f05d..65b7bf7 100644
--- a/README.md
+++ b/README.md
@@ -8,15 +8,18 @@ HyperLPR是一个使用深度学习针对对中文车牌识别的实现，与较
 
 ### 更新
 
++ 添加端到端的序列识别模型识别率大幅度提升
++ 添加的端到端模型可以识别 新能源车牌，教练车牌，白色警用车牌
 + 更新Windows版本的Visual Studio工程。（2017.11.15）
 
 
-+ 增加cpp版本,目前仅支持标准蓝牌(需要依赖OpenCV 3.3)。
-+ 添加了简单的Android实现 (骁龙835 (*720*x*1280*)  200ms)。
++ 增加cpp版本,目前仅支持标准蓝牌(需要依赖OpenCV 3.3)
++ 添加了简单的Android实现 (骁龙835 (*720*x*1280*)  200ms)
 
 ### 特性
 
 + 速度快 720p ，单核 Intel 2.2G CPU (macbook Pro 2015)识别时间 <=140ms 。
++ 基于端到端的车牌识别无需进行字符分割。
 + 识别率高  EasyPR数据集上0-error达到 81.75%, 1-error识别率达到 94.1%。
 + 轻量 总代码量不超1k行。
 
@@ -81,13 +84,14 @@ sudo make -j
 
 - [x] 单行蓝牌
 - [x] 单行黄牌
-- [ ] 新能源车牌
+- [x] 新能源车牌
+- [x] 白色警用车牌
+- [x] 使馆/港澳车牌
+- [x] 教练车牌
 - [ ] 双层黄牌
 - [ ] 双层武警
 - [ ] 双层军牌
 - [ ] 农用车牌
-- [ ] 白色警用车牌
-- [ ] 使馆/港澳车牌
 - [ ] 民航车牌
 - [ ] 个性化车牌
 
diff --git a/hyperlpr/e2e.py b/hyperlpr/e2e.py
new file mode 100755
index 0000000..76b96b8
--- /dev/null
+++ b/hyperlpr/e2e.py
@@ -0,0 +1,64 @@
+#coding=utf-8
+from keras import backend as K
+from keras.models import load_model
+from keras.layers import *
+from captcha.image import ImageCaptcha
+import numpy as np
+import random
+import string
+
+import cv2
+import e2emodel as model
+chars = [u"京", u"沪", u"津", u"渝", u"冀", u"晋", u"蒙", u"辽", u"吉", u"黑", u"苏", u"浙", u"皖", u"闽", u"赣", u"鲁", u"豫", u"鄂", u"湘", u"粤", u"桂",
+             u"琼", u"川", u"贵", u"云", u"藏", u"陕", u"甘", u"青", u"宁", u"新", u"0", u"1", u"2", u"3", u"4", u"5", u"6", u"7", u"8", u"9", u"A",
+             u"B", u"C", u"D", u"E", u"F", u"G", u"H", u"J", u"K", u"L", u"M", u"N", u"P", u"Q", u"R", u"S", u"T", u"U", u"V", u"W", u"X",
+             u"Y", u"Z",u"港",u"学",u"使",u"警",u"澳",u"挂",u"军",u"北",u"南",u"广",u"沈",u"兰",u"成",u"济",u"海",u"民",u"航",u"空"
+             ];
+pred_model = model.construct_model("./model/ocr_plate_all_w_rnn_2.h5",)
+import time
+
+
+
+def fastdecode(y_pred):
+    results = ""
+    confidence = 0.0
+    table_pred = y_pred.reshape(-1, len(chars)+1)
+
+    res = table_pred.argmax(axis=1)
+
+    for i,one in enumerate(res):
+        if one<len(chars) and (i==0 or (one!=res[i-1])):
+            results+= chars[one]
+            confidence+=table_pred[i][one]
+    confidence/= len(results)
+    return results,confidence
+
+def recognizeOne(src):
+    # x_tempx= cv2.imread(src)
+    x_tempx = src
+    # x_tempx = cv2.bitwise_not(x_tempx)
+    x_temp = cv2.resize(x_tempx,( 160,40))
+    x_temp = x_temp.transpose(1, 0, 2)
+    t0 = time.time()
+    y_pred = pred_model.predict(np.array([x_temp]))
+    y_pred = y_pred[:,2:,:]
+    # plt.imshow(y_pred.reshape(16,66))
+    # plt.show()
+
+    #
+    # cv2.imshow("x_temp",x_tempx)
+    # cv2.waitKey(0)
+    return fastdecode(y_pred)
+#
+#
+# import os
+#
+# path = "/Users/yujinke/PycharmProjects/HyperLPR_Python_web/cache/finemapping"
+# for filename in os.listdir(path):
+#     if filename.endswith(".png") or filename.endswith(".jpg") or filename.endswith(".bmp"):
+#         x = os.path.join(path,filename)
+#         recognizeOne(x)
+#         # print time.time() - t0
+#
+#         # cv2.imshow("x",x)
+#         # cv2.waitKey()
diff --git a/hyperlpr/e2emodel.py b/hyperlpr/e2emodel.py
new file mode 100644
index 0000000..dcbb60a
--- /dev/null
+++ b/hyperlpr/e2emodel.py
@@ -0,0 +1,35 @@
+
+from keras import backend as K
+from keras.models import *
+from keras.layers import *
+from captcha.image import ImageCaptcha
+import e2e
+
+
+def ctc_lambda_func(args):
+    y_pred, labels, input_length, label_length = args
+    y_pred = y_pred[:, 2:, :]
+    return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
+
+
+def construct_model(model_path):
+    input_tensor = Input((None, 40, 3))
+    x = input_tensor
+    base_conv = 32
+
+    for i in range(3):
+        x = Conv2D(base_conv * (2 ** (i)), (3, 3),padding="same")(x)
+        x = BatchNormalization()(x)
+        x = Activation('relu')(x)
+        x = MaxPooling2D(pool_size=(2, 2))(x)
+    x = Conv2D(256, (5, 5))(x)
+    x = BatchNormalization()(x)
+    x = Activation('relu')(x)
+    x = Conv2D(1024, (1, 1))(x)
+    x = BatchNormalization()(x)
+    x = Activation('relu')(x)
+    x = Conv2D(len(e2e.chars)+1, (1, 1))(x)
+    x = Activation('softmax')(x)
+    base_model = Model(inputs=input_tensor, outputs=x)
+    base_model.load_weights(model_path)
+    return base_model
\ No newline at end of file
diff --git a/hyperlpr/pipline.py b/hyperlpr/pipline.py
index c621191..62de428 100644
--- a/hyperlpr/pipline.py
+++ b/hyperlpr/pipline.py
@@ -22,7 +22,7 @@ sys.setdefaultencoding("utf-8")
 
 fontC = ImageFont.truetype("./Font/platech.ttf", 14, 0);
 
-
+import e2e
 #寻找车牌左右边界
 
 def find_edge(image):
@@ -91,7 +91,7 @@ def horizontalSegmentation(image):
 #打上boundingbox和标签
 def drawRectBox(image,rect,addText):
     cv2.rectangle(image, (int(rect[0]), int(rect[1])), (int(rect[0] + rect[2]), int(rect[1] + rect[3])), (0,0, 255), 2,cv2.LINE_AA)
-    cv2.rectangle(image, (int(rect[0]-1), int(rect[1])-16), (int(rect[0] + 80), int(rect[1])), (0, 0, 255), -1,
+    cv2.rectangle(image, (int(rect[0]-1), int(rect[1])-16), (int(rect[0] + 115), int(rect[1])), (0, 0, 255), -1,
                   cv2.LINE_AA)
 
     img = Image.fromarray(image)
@@ -129,7 +129,7 @@ def RecognizePlateJson(image):
 
 
         ptype = td.SimplePredict(plate)
-        if ptype>0 and ptype<5:
+        if ptype>0 and ptype<4:
             plate = cv2.bitwise_not(plate)
         # demo = verticalEdgeDetection(plate)
 
@@ -137,7 +137,7 @@ def RecognizePlateJson(image):
         image_rgb = fv.finemappingVertical(image_rgb)
         cache.verticalMappingToFolder(image_rgb)
         # print time.time() - t1,"校正"
-
+        print "e2e:",e2e.recognizeOne(image_rgb)[0]
         image_gray = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY)
 
 
@@ -184,6 +184,40 @@ def RecognizePlateJson(image):
 
 
 
+def SimpleRecognizePlateByE2E(image):
+    t0 = time.time()
+    images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1)
+    res_set = []
+    for j,plate in enumerate(images):
+        plate, rect, origin_plate  =plate
+        # plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY)
+        plate  =cv2.resize(plate,(136,36*2))
+        res,confidence = e2e.recognizeOne(origin_plate)
+        print "res",res
+
+        t1 = time.time()
+        ptype = td.SimplePredict(plate)
+        if ptype>0 and ptype<5:
+            # pass
+            plate = cv2.bitwise_not(plate)
+        image_rgb = fm.findContoursAndDrawBoundingBox(plate)
+        image_rgb = fv.finemappingVertical(image_rgb)
+        image_rgb = fv.finemappingVertical(image_rgb)
+        cache.verticalMappingToFolder(image_rgb)
+        cv2.imwrite("./"+str(j)+".jpg",image_rgb)
+        res,confidence = e2e.recognizeOne(image_rgb)
+        print res,confidence
+        res_set.append([[],res,confidence])
+
+        if confidence>0.7:
+            image = drawRectBox(image, rect, res+" "+str(round(confidence,3)))
+    return image,res_set
+
+
+
+
+
+
 
 def SimpleRecognizePlate(image):
     t0 = time.time()
@@ -200,8 +234,10 @@ def SimpleRecognizePlate(image):
             plate = cv2.bitwise_not(plate)
 
         image_rgb = fm.findContoursAndDrawBoundingBox(plate)
+
         image_rgb = fv.finemappingVertical(image_rgb)
         cache.verticalMappingToFolder(image_rgb)
+        print "e2e:", e2e.recognizeOne(image_rgb)
         image_gray = cv2.cvtColor(image_rgb,cv2.COLOR_RGB2GRAY)
 
         # image_gray = horizontalSegmentation(image_gray)