diff --git a/OCR YOLO11/yolomain.py b/OCR YOLO11/yolomain.py
index 19caf18..742eba2 100644
--- a/OCR YOLO11/yolomain.py	
+++ b/OCR YOLO11/yolomain.py	
@@ -2,7 +2,10 @@ from ultralytics import YOLO
 from PIL import Image
 from transformers import TrOCRProcessor, VisionEncoderDecoderModel
 import os
-os.chdir("C:/Users/celma/OneDrive - Hanze/School/periode 1.4/IOT/YOLO11/License Plate Recognition.v11i.yolov11")
+
+os.chdir(
+    "C:/Users/celma/OneDrive - Hanze/School/periode 1.4/IOT/YOLO11/License Plate Recognition.v11i.yolov11"
+)
 
 
 model = YOLO("license_plate_detector.pt")
@@ -17,18 +20,13 @@ clss = results[0].boxes.cls.cpu().tolist()
 print(boxes)
 
 if boxes is not None:
-    for box, cls in zip(boxes,clss):
-        crop_obj = boxes[int(box[1]):int(box[3]) + int(box[0]):int(box[2])]
+    for box, cls in zip(boxes, clss):
+        crop_obj = boxes[int(box[1]) : int(box[3]) + int(box[0]) : int(box[2])]
 
-cropped_img = img.crop(
-    (int(box[0]), int(box[1]), int(box[2]), int(box[3]))
-)
+cropped_img = img.crop((int(box[0]), int(box[1]), int(box[2]), int(box[3])))
 
 cropped_img.show()
 
-processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
-model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-large-printed")
-
 image = cropped_img.convert("RGB")
 pixel_values = processor(images=image, return_tensors="pt").pixel_values
 generated_ids = model.generate(pixel_values)
diff --git a/test2.py b/test2.py
new file mode 100644
index 0000000..7aac140
--- /dev/null
+++ b/test2.py
@@ -0,0 +1,29 @@
+from ultralytics import YOLO
+from PIL import Image
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+import os
+from datetime import datetime
+import torch
+
+print("11")
+processor = TrOCRProcessor.from_pretrained(
+    "microsoft/trocr-base-handwritten", use_fast=True
+)
+print("12")
+model = VisionEncoderDecoderModel.from_pretrained(
+    "microsoft/trocr-Large-printed"
+)
+print("13")
+
+cropped_img = Image.open("license_plate.jpg")
+cropped_img.show()
+
+
+image = cropped_img.convert("RGB")
+first = datetime.now()
+pixel_values = processor(images=image, return_tensors="pt").pixel_values
+generated_ids = model.generate(pixel_values)
+print(generated_ids)
+text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(datetime.now() - first)
+print(text)