diff --git a/ddddocr/__init__.py b/ddddocr/__init__.py
index eac66cc..41aa654 100644
--- a/ddddocr/__init__.py
+++ b/ddddocr/__init__.py
@@ -5,7 +5,7 @@ warnings.filterwarnings('ignore')
 import io
 import os
 import base64
-import json
+import pathlib
 import onnxruntime
 from PIL import Image, ImageChops
 import numpy as np
@@ -29,26 +29,13 @@ class TypeError(Exception):
 
 class DdddOcr(object):
     def __init__(self, ocr: bool = True, det: bool = False, old: bool = False, use_gpu: bool = False,
-                 device_id: int = 0, show_ad=True, import_onnx_path: str = "", charsets_path: str = ""):
+                 device_id: int = 0, show_ad=True):
         if show_ad:
             print("欢迎使用ddddocr，本项目专注带动行业内卷，个人博客:wenanzhe.com")
             print("训练数据支持来源于:http://146.56.204.113:19199/preview")
             print("爬虫框架feapder可快速一键接入，快速开启爬虫之旅：https://github.com/Boris-code/feapder")
-        self.use_import_onnx = False
-        self.__word = False
-        self.__resize = []
-        self.__channel = 1
-        if import_onnx_path != "":
-            det = False
-            ocr = False
-            self.__graph_path = import_onnx_path
-            with open(charsets_path, 'r', encoding="utf-8") as f:
-                info = json.loads(f.read())
-            self.__charset = info['charset']
-            self.__word = info['word']
-            self.__resize = info['image']
-            self.__channel = info['channel']
-            self.use_import_onnx = True
+
+
         if det:
             ocr = False
             print("开启det后自动关闭ocr")
@@ -1453,7 +1440,7 @@ class DdddOcr(object):
             self.__providers = [
                 'CPUExecutionProvider',
             ]
-        if ocr or det or self.use_import_onnx:
+        if ocr or det:
             self.__ort_session = onnxruntime.InferenceSession(self.__graph_path, providers=self.__providers)
 
     def preproc(self, img, input_size, swap=(2, 0, 1)):
@@ -1594,53 +1581,35 @@ class DdddOcr(object):
             return []
         return result
 
-    def classification(self, img_bytes: bytes = None, img_base64: str = None):
+    def classification(self, img):
         if self.det:
             raise TypeError("当前识别类型为目标检测")
-        if img_bytes:
-            image = Image.open(io.BytesIO(img_bytes))
+        if not isinstance(img, (bytes, str, pathlib.PurePath, Image.Image)):
+            raise TypeError("未知图片类型")
+        if isinstance(img, bytes):
+            image = Image.open(io.BytesIO(img))
+        elif isinstance(img, Image.Image):
+            image = img.copy()
+        elif isinstance(img, str):
+            image = base64_to_image(img)
         else:
-            image = base64_to_image(img_base64)
-        if not self.use_import_onnx:
-            image = image.resize((int(image.size[0] * (64 / image.size[1])), 64), Image.ANTIALIAS).convert('L')
-        else:
-            if self.__resize[0] == -1:
-                if self.__word:
-                    image = image.resize((self.__resize[1], self.__resize[1]), Image.ANTIALIAS)
-                else:
-                    image = image.resize((int(image.size[0] * (self.__resize[1] / image.size[1])), self.__resize[1]), Image.ANTIALIAS)
-            else:
-                image = image.resize((self.__resize[0], self.__resize[1]), Image.ANTIALIAS)
-            if self.__channel == 1:
-                image = image.convert('L')
-            else:
-                image = image.convert('RGB')
+            assert isinstance(img, pathlib.PurePath)
+            image = Image.open(img)
+        image = image.resize((int(image.size[0] * (64 / image.size[1])), 64), Image.ANTIALIAS).convert('L')
         image = np.array(image).astype(np.float32)
         image = np.expand_dims(image, axis=0) / 255.
-        if not self.use_import_onnx:
-            image = (image - 0.5) / 0.5
-        else:
-            if self.__channel == 1:
-                image = (image - 0.456) / 0.224
-            else:
-                image = (image - np.array([0.485, 0.456, 0.406])) / np.array([0.229, 0.224, 0.225])
-
+        image = (image - 0.5) / 0.5
         ort_inputs = {'input1': np.array([image])}
         ort_outs = self.__ort_session.run(None, ort_inputs)
         result = []
-
         last_item = 0
-        if self.__word:
-            for item in ort_outs[1]:
+        for item in ort_outs[0][0]:
+            if item == last_item:
+                continue
+            else:
+                last_item = item
+            if item != 0:
                 result.append(self.__charset[item])
-        else:
-            for item in ort_outs[0][0]:
-                if item == last_item:
-                    continue
-                else:
-                    last_item = item
-                if item != 0:
-                    result.append(self.__charset[item])
 
         return ''.join(result)