diff --git a/demo/remote_request.py b/demo/remote_request.py
index 32c1730..95f3595 100644
--- a/demo/remote_request.py
+++ b/demo/remote_request.py
@@ -4,7 +4,7 @@ import sys
 import os
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
-from utils import get_som_labeled_img, get_caption_model_processor, get_yolo_model, get_ocr_bbox
+from utils import get_som_labeled_img, get_caption_model_processor, get_yolo_model, check_ocr_box
 import torch
 from PIL import Image
 from typing import Dict, Tuple, List
@@ -45,7 +45,7 @@ class Omniparser(object):
         }
         BOX_TRESHOLD = config['BOX_TRESHOLD']
 
-        text, ocr_bbox = get_ocr_bbox(image)
+        (text, ocr_bbox), _ = check_ocr_box(image, display_img=False, output_bb_format='xyxy', easyocr_args={'text_threshold': 0.8}, use_paddleocr=False)
         dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image, self.som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=self.caption_model_processor, ocr_text=text,use_local_semantics=True, iou_threshold=0.7, scale_img=False, batch_size=128)
 
         return dino_labled_img, parsed_content_list
diff --git a/utils.py b/utils.py
index a4e3f00..1da7e68 100755
--- a/utils.py
+++ b/utils.py
@@ -501,50 +501,52 @@ def get_xywh_yolo(input):
     x, y, w, h = input[0], input[1], input[2] - input[0], input[3] - input[1]
     x, y, w, h = int(x), int(y), int(w), int(h)
     return x, y, w, h
-    
 
-
-def check_ocr_box(image_path, display_img = True, output_bb_format='xywh', goal_filtering=None, easyocr_args=None, use_paddleocr=False):
+def check_ocr_box(image_source: Union[str, Image.Image], display_img = True, output_bb_format='xywh', goal_filtering=None, easyocr_args=None, use_paddleocr=False):
+    if isinstance(image_source, str):
+        image_source = Image.open(image_source)
+    if image_source.mode == 'RGBA':
+        # Convert RGBA to RGB to avoid alpha channel issues
+        image_source = image_source.convert('RGB')
+    image_np = np.array(image_source)
+    w, h = image_source.size
     if use_paddleocr:
         if easyocr_args is None:
             text_threshold = 0.5
         else:
             text_threshold = easyocr_args['text_threshold']
-        result = paddle_ocr.ocr(image_path, cls=False)[0]
-        # conf = [item[1] for item in result]
+        result = paddle_ocr.ocr(image_np, cls=False)[0]
         coord = [item[0] for item in result if item[1][1] > text_threshold]
         text = [item[1][0] for item in result if item[1][1] > text_threshold]
     else:  # EasyOCR
         if easyocr_args is None:
             easyocr_args = {}
-        result = reader.readtext(image_path, **easyocr_args)
-        # print('goal filtering pred:', result[-5:])
+        result = reader.readtext(image_np, **easyocr_args)
         coord = [item[0] for item in result]
         text = [item[1] for item in result]
-    # read the image using cv2
     if display_img:
-        opencv_img = cv2.imread(image_path)
-        opencv_img = cv2.cvtColor(opencv_img, cv2.COLOR_RGB2BGR)
+        opencv_img = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
         bb = []
         for item in coord:
             x, y, a, b = get_xywh(item)
-            # print(x, y, a, b)
             bb.append((x, y, a, b))
             cv2.rectangle(opencv_img, (x, y), (x+a, y+b), (0, 255, 0), 2)
-        
-        # Display the image
-        plt.imshow(opencv_img)
+        #  matplotlib expects RGB
+        plt.imshow(cv2.cvtColor(opencv_img, cv2.COLOR_BGR2RGB))
     else:
         if output_bb_format == 'xywh':
             bb = [get_xywh(item) for item in coord]
         elif output_bb_format == 'xyxy':
             bb = [get_xyxy(item) for item in coord]
-        # print('bounding box!!!', bb)
     return (text, bb), goal_filtering
 
-def get_ocr_bbox(image):
+def get_ocr_bbox(image: Image.Image):
+    if image.mode == 'RGBA':
+        # Convert RGBA to RGB to avoid alpha channel issues
+        image = image.convert('RGB')
+    image_np = np.array(image)
+    result = paddle_ocr.ocr(image_np, cls=False)[0]
     text_threshold = 0.8
-    result = paddle_ocr.ocr(image, cls=False)[0]
     coord = [item[0] for item in result if item[1][1] > text_threshold]
     text = [item[1][0] for item in result if item[1][1] > text_threshold]
     bb = [get_xyxy(item) for item in coord]