diff --git a/requirements-dev.txt b/requirements-dev.txt
index fcaa0d0..92a91d9 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -2,4 +2,5 @@
 fiftyone==1.2.0
 jupyter==1.1.1
 onnx==1.17.0
-torch==2.5.1
\ No newline at end of file
+torch==2.5.1
+torchvision==0.20.1
diff --git a/requirements.txt b/requirements.txt
index 1a6079b..df929a9 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,5 +5,4 @@ opencv-python==4.10.0.84
 PyMuPDF==1.25.1
 PyYAML==6.0.2
 scipy==1.14.1
-PySide6==6.8.1
-torchvision==0.20.1
\ No newline at end of file
+PySide6==6.8.1
\ No newline at end of file
diff --git a/scripts/output.bak b/scripts/output.bak
new file mode 100644
index 0000000..9c59369
--- /dev/null
+++ b/scripts/output.bak
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+  - label: martyria_diatonic_ke
+    confidence: 0.965539276599884
+    line: 12
+    bounding_rect:
+      x: 1162
+      y: 2535
+      w: 22
+      h: 38
+    bounding_circle:
+      x: 1173.0
+      y: 2552.20263671875
+      r: 19.898107528686523
+  - label: oligon
+    confidence: 0.9123576879501343
+    line: 12
+    bounding_rect:
+      x: 1209
+      y: 2463
+      w: 94
+      h: 11
+    bounding_circle:
+      x: 1255.5
+      y: 2468.0
+      r: 46.76814270019531
+  - label: fthora_hard_chromatic_di
+    confidence: 0.41087907552719116
+    line: 12
+    bounding_rect:
+      x: 1236
+      y: 2519
+      w: 18
+      h: 38
+    bounding_circle:
+      x: 1246.0
+      y: 2537.5
+      r: 18.607892990112305
+  - label: klasma
+    confidence: 0.9996312856674194
+    line: 12
+    bounding_rect:
+      x: 1244
+      y: 2438
+      w: 38
+      h: 16
+    bounding_circle:
+      x: 1262.5
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+      r: 19.039531707763672
+  - label: apli
+    confidence: 0.8788579702377319
+    line: 12
+    bounding_rect:
+      x: 1283
+      y: 2377
+      w: 7
+      h: 14
+    bounding_circle:
+      x: 1286.0
+      y: 2383.5
+      r: 6.800835609436035
+  - label: elafron
+    confidence: 0.9812929630279541
+    line: 12
+    bounding_rect:
+      x: 1312
+      y: 2452
+      w: 74
+      h: 33
+    bounding_circle:
+      x: 1348.5
+      y: 2473.5
+      r: 37.47675704956055
+  - label: null
+    confidence: 0
+    line: 12
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+      x: 1358
+      y: 2605
+      w: 4
+      h: 4
+    bounding_circle:
+      x: 1359.5
+      y: 2606.5
+      r: 2.12142014503479
+  - label: ison
+    confidence: 0.999593198299408
+    line: 12
+    bounding_rect:
+      x: 1394
+      y: 2443
+      w: 99
+      h: 36
+    bounding_circle:
+      x: 1443.0
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+      r: 51.10049057006836
+  - label: null
+    confidence: 0
+    line: 12
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+      x: 1405
+      y: 2395
+      w: 3
+      h: 4
+    bounding_circle:
+      x: 1406.0
+      y: 2396.5
+      r: 1.8028756380081177
+  - label: apostrofos
+    confidence: 0.36691704392433167
+    line: 12
+    bounding_rect:
+      x: 1420
+      y: 2518
+      w: 20
+      h: 48
+    bounding_circle:
+      x: 1429.5
+      y: 2541.5
+      r: 24.135135650634766
+  - label: null
+    confidence: 0
+    line: 12
+    bounding_rect:
+      x: 1469
+      y: 2591
+      w: 3
+      h: 4
+    bounding_circle:
+      x: 1470.0
+      y: 2592.5
+      r: 1.8028756380081177
+  - label: oligon
+    confidence: 0.994918704032898
+    line: 12
+    bounding_rect:
+      x: 1501
+      y: 2463
+      w: 118
+      h: 16
+    bounding_circle:
+      x: 1559.5
+      y: 2470.0
+      r: 58.917415618896484
+  - label: kentima
+    confidence: 0.9745245575904846
+    line: 12
+    bounding_rect:
+      x: 1545
+      y: 2433
+      w: 22
+      h: 23
+    bounding_circle:
+      x: 1555.5
+      y: 2444.5
+      r: 14.849342346191406
+  - label: ison
+    confidence: 0.9951184391975403
+    line: 12
+    bounding_rect:
+      x: 1622
+      y: 2442
+      w: 109
+      h: 34
+    bounding_circle:
+      x: 1676.0
+      y: 2459.5
+      r: 55.662025451660156
+  - label: fthora_hard_chromatic_di
+    confidence: 0.3982745110988617
+    line: 12
+    bounding_rect:
+      x: 1649
+      y: 2518
+      w: 18
+      h: 37
+    bounding_circle:
+      x: 1658.5
+      y: 2536.0
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diff --git a/src/model.py b/src/model.py
index 1b39647..9f25519 100644
--- a/src/model.py
+++ b/src/model.py
@@ -1,7 +1,6 @@
 import json
+import numpy as np
 import onnxruntime as ort
-from torchvision import models
-from torchvision import transforms
 
 
 def load_onnx_model(model_path):
@@ -13,11 +12,17 @@ def load_classes(classes_path):
         return json.load(f)
 
 
-def get_transform():
-    return transforms.Compose(
-        [
-            transforms.Resize((224, 224)),
-            transforms.ToTensor(),
-            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-        ]
-    )
+def transform(img):
+    transformed = img.copy()
+    transformed = transformed / 255.0  # Normalize to [0, 1]
+    transformed = (transformed - np.array([0.485, 0.456, 0.406])) / np.array(
+        [0.229, 0.224, 0.225]
+    )  # Apply mean/std
+
+    # Rearrange the dimensions to (channels, height, width)
+    transformed = np.transpose(transformed, (2, 0, 1))  # Convert HWC to CHW
+
+    # Add the batch dimension (1, channels, height, width)
+    transformed = np.expand_dims(transformed, axis=0)
+
+    return transformed
diff --git a/src/ocr.py b/src/ocr.py
index d42a8b3..409b800 100644
--- a/src/ocr.py
+++ b/src/ocr.py
@@ -6,7 +6,7 @@
 from PIL import Image
 
 import util
-from model import get_transform
+from model import transform
 from segmentation import segment
 from text_removal import remove_text
 
@@ -289,18 +289,15 @@ def sort_matches(matches):
 
 
 def recognize_contours(matches, model, classes):
-    transform = get_transform()
-
     for m in matches:
         if m.test_image is None or m.test_image.size == 0:
             continue
 
         img = cv2.cvtColor(m.test_image, cv2.COLOR_GRAY2RGB)
-        img = Image.fromarray(img)
 
-        tensor = transform(img).unsqueeze(0)
+        img = transform(img)
 
-        output = model.run(["output"], {"input": tensor.numpy()})
+        output = model.run(["output"], {"input": img.astype(np.float32)})
 
         probabilities = np.exp(output[0][0]) / np.sum(np.exp(output[0][0]))  # Softmax
         class_id = np.argmax(probabilities)