diff --git a/gradio_demo.py b/gradio_demo.py
index 11f3324..0ecc5b8 100644
--- a/gradio_demo.py
+++ b/gradio_demo.py
@@ -58,14 +58,15 @@ DEVICE = torch.device('cuda')
 def process(
     image_input,
     box_threshold,
-    iou_threshold
+    iou_threshold,
+    use_paddleocr
 ) -> Optional[Image.Image]:
 
     image_save_path = 'imgs/saved_image_demo.png'
     image_input.save(image_save_path)
     # import pdb; pdb.set_trace()
 
-    ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_save_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.9})
+    ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_save_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.9}, use_paddleocr=use_paddleocr)
     text, ocr_bbox = ocr_bbox_rslt
     # print('prompt:', prompt)
     dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_save_path, yolo_model, BOX_TRESHOLD = box_threshold, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=caption_model_processor, ocr_text=text,iou_threshold=iou_threshold)
@@ -88,6 +89,8 @@ with gr.Blocks() as demo:
             # set the threshold for removing the bounding boxes with large overlap, default is 0.1
             iou_threshold_component = gr.Slider(
                 label='IOU Threshold', minimum=0.01, maximum=1.0, step=0.01, value=0.1)
+            use_paddleocr_component = gr.Checkbox(
+                label='Use PaddleOCR', default=True)
             submit_button_component = gr.Button(
                 value='Submit', variant='primary')
         with gr.Column():
@@ -99,7 +102,8 @@ with gr.Blocks() as demo:
         inputs=[
             image_input_component,
             box_threshold_component,
-            iou_threshold_component
+            iou_threshold_component,
+            use_paddleocr_component
         ],
         outputs=[image_output_component, text_output_component]
     )
diff --git a/requirements.txt b/requirements.txt
index cddf7fd..08bfc38 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -14,3 +14,5 @@ dill
 accelerate
 timm
 einops==0.8.0
+paddlepaddle
+paddleocr
\ No newline at end of file
diff --git a/utils.py b/utils.py
index b56588e..13b3c9e 100755
--- a/utils.py
+++ b/utils.py
@@ -18,7 +18,17 @@ import numpy as np
 # %matplotlib inline
 from matplotlib import pyplot as plt
 import easyocr
+from paddleocr import PaddleOCR
 reader = easyocr.Reader(['en'])
+paddle_ocr = PaddleOCR(
+    lang='en',  # other lang also available
+    use_angle_cls=False,
+    use_gpu=False,  # using cuda will conflict with pytorch in the same process
+    show_log=False,
+    max_batch_size=1024,
+    use_dilation=True,  # improves accuracy
+    det_db_score_mode='slow',  # improves accuracy
+    rec_batch_num=1024)
 import time
 import base64
 
@@ -370,14 +380,18 @@ def get_xywh_yolo(input):
     
 
 
-def check_ocr_box(image_path, display_img = True, output_bb_format='xywh', goal_filtering=None, easyocr_args=None):
-    if easyocr_args is None:
-        easyocr_args = {}
-    result = reader.readtext(image_path, **easyocr_args)
-    is_goal_filtered = False
-    # print('goal filtering pred:', result[-5:])
-    coord = [item[0] for item in result]
-    text = [item[1] for item in result]
+def check_ocr_box(image_path, display_img = True, output_bb_format='xywh', goal_filtering=None, easyocr_args=None, use_paddleocr=False):
+    if use_paddleocr:
+        result = paddle_ocr.ocr(image_path, cls=False)[0]
+        coord = [item[0] for item in result]
+        text = [item[1][0] for item in result]
+    else:  # EasyOCR
+        if easyocr_args is None:
+            easyocr_args = {}
+        result = reader.readtext(image_path, **easyocr_args)
+        # print('goal filtering pred:', result[-5:])
+        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)