diff --git a/OCR YOLO11/yolomain.py b/OCR YOLO11/yolomain.py
index e501f7d..19caf18 100644
--- a/OCR YOLO11/yolomain.py	
+++ b/OCR YOLO11/yolomain.py	
@@ -1,37 +1,36 @@
 from ultralytics import YOLO
-from ultralytics.utils.downloads import safe_download
-from ultralytics.utils.plotting import Annotator, colors
 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")
 
-crop_dir_name = "C:/Users/celma/OneDrive - Hanze/School/periode 1.4/IOT/YOLO11/License Plate Recognition.v11i.yolov11/test/crops"
-if not os.path.exists(crop_dir_name):
-    os.makedirs(crop_dir_name)
 
-model = YOLO("license_plate_detector.pt") # Loads the pre-trained YOLO11 model
+model = YOLO("license_plate_detector.pt")
 names = model.names
 
 
-results = model.predict("test/images/000c.jpg", show=True, save=True)
-img = Image.open("test/images/000c.jpg")  # Load the original image using PIL
+results = model.predict("test/images/000i.jpg", show=False, save=False)
+img = Image.open("test/images/000i.jpg")
 boxes = results[0].boxes.xyxy.cpu().tolist()
 clss = results[0].boxes.cls.cpu().tolist()
-# annotator = Annotator(results[0], line_width=2, example=names)
 
-#print(results)
 print(boxes)
 
 if boxes is not None:
     for box, cls in zip(boxes,clss):
-    #   annotator.box_label(box, color=colors(int(cls), True), label=names(int(cls)))
-
         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]))
 )
 
-save_path = os.path.join(crop_dir_name, "cropped_image.jpg")
-cropped_img.save("cropped_image.jpg")
-cropped_img.show()
\ No newline at end of file
+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)
+text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(text)