diff --git a/docs/source/modules/transforms.rst b/docs/source/modules/transforms.rst
index 7f90325e4d..7fc02f4cc4 100644
--- a/docs/source/modules/transforms.rst
+++ b/docs/source/modules/transforms.rst
@@ -28,6 +28,7 @@ Here are all transformations that are available through docTR:
 .. autoclass:: GaussianNoise
 .. autoclass:: RandomHorizontalFlip
 .. autoclass:: RandomShadow
+.. autoclass:: RandomResize
 
 
 Composing transformations
diff --git a/doctr/transforms/modules/pytorch.py b/doctr/transforms/modules/pytorch.py
index b2b9c435ee..3d449e9e4a 100644
--- a/doctr/transforms/modules/pytorch.py
+++ b/doctr/transforms/modules/pytorch.py
@@ -15,7 +15,7 @@
 
 from ..functional.pytorch import random_shadow
 
-__all__ = ["Resize", "GaussianNoise", "ChannelShuffle", "RandomHorizontalFlip", "RandomShadow"]
+__all__ = ["Resize", "GaussianNoise", "ChannelShuffle", "RandomHorizontalFlip", "RandomShadow", "RandomResize"]
 
 
 class Resize(T.Resize):
@@ -213,3 +213,38 @@ def __call__(self, x: torch.Tensor) -> torch.Tensor:
 
     def extra_repr(self) -> str:
         return f"opacity_range={self.opacity_range}"
+
+
+class RandomResize(torch.nn.Module):
+    """Randomly resize the input image and align corresponding targets
+
+    >>> import torch
+    >>> from doctr.transforms import RandomResize
+    >>> transfo = RandomResize((0.3, 0.9), p=0.5)
+    >>> out = transfo(torch.rand((3, 64, 64)))
+
+    Args:
+    ----
+        scale_range: range of the resizing factor for width and height (independently)
+        p: probability to apply the transformation
+    """
+
+    def __init__(self, scale_range: Tuple[float, float] = (0.3, 0.9), p: float = 0.5) -> None:
+        super().__init__()
+        self.scale_range = scale_range
+        self.p = p
+        self._resize = Resize
+
+    def forward(self, img: torch.Tensor, target: np.ndarray) -> Tuple[torch.Tensor, np.ndarray]:
+        if torch.rand(1) < self.p:
+            scale_h = np.random.uniform(*self.scale_range)
+            scale_w = np.random.uniform(*self.scale_range)
+            new_size = (int(img.shape[-2] * scale_h), int(img.shape[-1] * scale_w))
+
+            _img, _target = self._resize(new_size, preserve_aspect_ratio=True, symmetric_pad=True)(img, target)
+
+            return _img, _target
+        return img, target
+
+    def extra_repr(self) -> str:
+        return f"scale_range={self.scale_range}, p={self.p}"
diff --git a/doctr/transforms/modules/tensorflow.py b/doctr/transforms/modules/tensorflow.py
index 5da1efd696..cfe13c4ea9 100644
--- a/doctr/transforms/modules/tensorflow.py
+++ b/doctr/transforms/modules/tensorflow.py
@@ -30,6 +30,7 @@
     "GaussianNoise",
     "RandomHorizontalFlip",
     "RandomShadow",
+    "RandomResize",
 ]
 
 
@@ -515,3 +516,38 @@ def __call__(self, x: tf.Tensor) -> tf.Tensor:
 
     def extra_repr(self) -> str:
         return f"opacity_range={self.opacity_range}"
+
+
+class RandomResize(NestedObject):
+    """Randomly resize the input image and align corresponding targets
+
+    >>> import tensorflow as tf
+    >>> from doctr.transforms import RandomResize
+    >>> transfo = RandomResize((0.3, 0.9), p=0.5)
+    >>> out = transfo(tf.random.uniform(shape=[64, 64, 3], minval=0, maxval=1))
+
+    Args:
+    ----
+        scale_range: range of the resizing factor for width and height (independently)
+        p: probability to apply the transformation
+    """
+
+    def __init__(self, scale_range: Tuple[float, float] = (0.3, 0.9), p: float = 0.5) -> None:
+        super().__init__()
+        self.scale_range = scale_range
+        self.p = p
+        self._resize = Resize
+
+    def __call__(self, img: tf.Tensor, target: np.ndarray) -> Tuple[tf.Tensor, np.ndarray]:
+        if np.random.rand(1) <= self.p:
+            scale_h = random.uniform(*self.scale_range)
+            scale_w = random.uniform(*self.scale_range)
+            new_size = (int(img.shape[-3] * scale_h), int(img.shape[-2] * scale_w))
+
+            _img, _target = self._resize(new_size, preserve_aspect_ratio=True, symmetric_pad=True)(img, target)
+
+            return _img, _target
+        return img, target
+
+    def extra_repr(self) -> str:
+        return f"scale_range={self.scale_range}, p={self.p}"
diff --git a/tests/pytorch/test_transforms_pt.py b/tests/pytorch/test_transforms_pt.py
index 0a81b93889..6fb6bcb88e 100644
--- a/tests/pytorch/test_transforms_pt.py
+++ b/tests/pytorch/test_transforms_pt.py
@@ -10,6 +10,7 @@
     GaussianNoise,
     RandomCrop,
     RandomHorizontalFlip,
+    RandomResize,
     RandomRotate,
     RandomShadow,
     Resize,
@@ -326,3 +327,26 @@ def test_random_shadow(input_dtype, input_shape):
         assert torch.all(transformed <= 255)
     else:
         assert torch.all(transformed <= 1.0)
+
+
+@pytest.mark.parametrize(
+    "p,target",
+    [
+        [1, np.array([[0.1, 0.1, 0.3, 0.4]], dtype=np.float32)],
+        [0, np.array([[0.1, 0.1, 0.3, 0.4]], dtype=np.float32)],
+        [1, np.array([[[0.1, 0.8], [0.3, 0.1], [0.3, 0.4], [0.8, 0.4]]], dtype=np.float32)],
+        [0, np.array([[[0.1, 0.8], [0.3, 0.1], [0.3, 0.4], [0.8, 0.4]]], dtype=np.float32)],
+    ],
+)
+def test_random_resize(p, target):
+    transfo = RandomResize(scale_range=(0.3, 1.3), p=p)
+    assert repr(transfo) == f"RandomResize(scale_range=(0.3, 1.3), p={p})"
+
+    img = torch.rand((3, 64, 64))
+    # Apply the transformation
+    out_img, out_target = transfo(img, target)
+    assert isinstance(out_img, torch.Tensor)
+    assert isinstance(out_target, np.ndarray)
+    # Resize is already well tested
+    assert torch.all(out_img == img) if p == 0 else out_img.shape != img.shape
+    assert out_target.shape == target.shape
diff --git a/tests/tensorflow/test_transforms_tf.py b/tests/tensorflow/test_transforms_tf.py
index 60c3179ed0..372814e371 100644
--- a/tests/tensorflow/test_transforms_tf.py
+++ b/tests/tensorflow/test_transforms_tf.py
@@ -468,3 +468,26 @@ def test_random_shadow(input_dtype, input_shape):
         assert tf.reduce_all(transformed <= 255)
     else:
         assert tf.reduce_all(transformed <= 1.0)
+
+
+@pytest.mark.parametrize(
+    "p,target",
+    [
+        [1, np.array([[0.1, 0.1, 0.3, 0.4]], dtype=np.float32)],
+        [0, np.array([[0.1, 0.1, 0.3, 0.4]], dtype=np.float32)],
+        [1, np.array([[[0.1, 0.8], [0.3, 0.1], [0.3, 0.4], [0.8, 0.4]]], dtype=np.float32)],
+        [0, np.array([[[0.1, 0.8], [0.3, 0.1], [0.3, 0.4], [0.8, 0.4]]], dtype=np.float32)],
+    ],
+)
+def test_random_resize(p, target):
+    transfo = T.RandomResize(scale_range=(0.3, 1.3), p=p)
+    assert repr(transfo) == f"RandomResize(scale_range=(0.3, 1.3), p={p})"
+
+    img = tf.random.uniform((64, 64, 3))
+    # Apply the transformation
+    out_img, out_target = transfo(img, target)
+    assert isinstance(out_img, tf.Tensor)
+    assert isinstance(out_target, np.ndarray)
+    # Resize is already well-tested
+    assert tf.reduce_all(tf.equal(out_img, img)) if p == 0 else out_img.shape != img.shape
+    assert out_target.shape == target.shape