diff --git a/python/p/kcsj.py b/python/p/kcsj.py
index 48d5264..22b5d4a 100644
--- a/python/p/kcsj.py
+++ b/python/p/kcsj.py
@@ -121,3 +121,142 @@ for i,j in enumerate(word_images):
     plt.imshow(word_images[i],cmap='gray')
 plt.show()
 
+#模版匹配
+# 准备模板(template[0-9]为数字模板；)
+template = ['0','1','2','3','4','5','6','7','8','9',
+            'A','B','C','D','E','F','G','H','J','K','L','M','N','P','Q','R','S','T','U','V','W','X','Y','Z',
+            '藏','川','鄂','甘','赣','贵','桂','黑','沪','吉','冀','津','晋','京','辽','鲁','蒙','闽','宁',
+            '青','琼','陕','苏','皖','湘','新','渝','豫','粤','云','浙']
+
+# 读取一个文件夹下的所有图片，输入参数是文件名，返回模板文件地址列表
+def read_directory(directory_name):
+    referImg_list = []
+    for filename in os.listdir(directory_name):
+        referImg_list.append(directory_name + "/" + filename)
+    return referImg_list
+
+# 获得中文模板列表（只匹配车牌的第一个字符）
+def get_chinese_words_list():
+    chinese_words_list = []
+    for i in range(34,64):
+        #将模板存放在字典中
+        c_word = read_directory('./refer1/'+ template[i])
+        chinese_words_list.append(c_word)
+    return chinese_words_list
+chinese_words_list = get_chinese_words_list()
+
+
+# 获得英文模板列表（只匹配车牌的第二个字符）
+def get_eng_words_list():
+    eng_words_list = []
+    for i in range(10,34):
+        e_word = read_directory('./refer1/'+ template[i])
+        eng_words_list.append(e_word)
+    return eng_words_list
+eng_words_list = get_eng_words_list()
+
+
+# 获得英文和数字模板列表（匹配车牌后面的字符）
+def get_eng_num_words_list():
+    eng_num_words_list = []
+    for i in range(0,34):
+        word = read_directory('./refer1/'+ template[i])
+        eng_num_words_list.append(word)
+    return eng_num_words_list
+eng_num_words_list = get_eng_num_words_list()
+
+
+# 读取一个模板地址与图片进行匹配，返回得分
+def template_score(template,image):
+    #将模板进行格式转换
+    template_img=cv2.imdecode(np.fromfile(template,dtype=np.uint8),1)
+    template_img = cv2.cvtColor(template_img, cv2.COLOR_RGB2GRAY)
+    #模板图像阈值化处理——获得黑白图
+    ret, template_img = cv2.threshold(template_img, 0, 255, cv2.THRESH_OTSU)
+#     height, width = template_img.shape
+#     image_ = image.copy()
+#     image_ = cv2.resize(image_, (width, height))
+    image_ = image.copy()
+    #获得待检测图片的尺寸
+    height, width = image_.shape
+    # 将模板resize至与图像一样大小
+    template_img = cv2.resize(template_img, (width, height))
+    # 模板匹配，返回匹配得分
+    result = cv2.matchTemplate(image_, template_img, cv2.TM_CCOEFF)
+    return result[0][0]
+
+
+# 对分割得到的字符逐一匹配
+def template_matching(word_images):
+    results = []
+    for index,word_image in enumerate(word_images):
+        if index==0:
+            best_score = []
+            for chinese_words in chinese_words_list:
+                score = []
+                for chinese_word in chinese_words:
+                    result = template_score(chinese_word,word_image)
+                    score.append(result)
+                best_score.append(max(score))
+            i = best_score.index(max(best_score))
+            # print(template[34+i])
+            r = template[34+i]
+            results.append(r)
+            continue
+        if index==1:
+            best_score = []
+            for eng_word_list in eng_words_list:
+                score = []
+                for eng_word in eng_word_list:
+                    result = template_score(eng_word,word_image)
+                    score.append(result)
+                best_score.append(max(score))
+            i = best_score.index(max(best_score))
+            # print(template[10+i])
+            r = template[10+i]
+            results.append(r)
+            continue
+        else:
+            best_score = []
+            for eng_num_word_list in eng_num_words_list:
+                score = []
+                for eng_num_word in eng_num_word_list:
+                    result = template_score(eng_num_word,word_image)
+                    score.append(result)
+                best_score.append(max(score))
+            i = best_score.index(max(best_score))
+            # print(template[i])
+            r = template[i]
+            results.append(r)
+            continue
+    return results
+
+
+word_images_ = word_images.copy()
+# 调用函数获得结果
+result = template_matching(word_images_)
+print(result)
+# "".join(result)函数将列表转换为拼接好的字符串，方便结果显示
+print( "".join(result))
+
+from PIL import ImageFont, ImageDraw, Image
+
+height,weight = origin_image.shape[0:2]
+print(height)
+print(weight)
+
+image_1 = origin_image.copy()
+cv2.rectangle(image_1, (int(0.2*weight), int(0.75*height)), (int(weight*0.9), int(height*0.95)), (0, 255, 0), 5)
+
+#设置需要显示的字体
+fontpath = "font/simsun.ttc"
+font = ImageFont.truetype(fontpath,64)
+img_pil = Image.fromarray(image_1)
+draw = ImageDraw.Draw(img_pil)
+
+#绘制文字信息
+draw.text((int(0.2*weight)+25, int(0.75*height)),  "".join(result), font = font, fill = (255, 255, 0))
+bk_img = np.array(img_pil)
+print(result)
+print( "".join(result))
+plt_show0(bk_img)