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diff --git a/text_recognizer/networks/mlp.py b/text_recognizer/networks/mlp.py
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+"""Defines the MLP network."""
+from typing import Callable, Dict, List, Optional, Union
+
+from einops.layers.torch import Rearrange
+import torch
+from torch import nn
+
+from text_recognizer.networks.util import activation_function
+
+
+class MLP(nn.Module):
+    """Multi layered perceptron network."""
+
+    def __init__(
+        self,
+        input_size: int = 784,
+        num_classes: int = 10,
+        hidden_size: Union[int, List] = 128,
+        num_layers: int = 3,
+        dropout_rate: float = 0.2,
+        activation_fn: str = "relu",
+    ) -> None:
+        """Initialization of the MLP network.
+
+        Args:
+            input_size (int): The input shape of the network. Defaults to 784.
+            num_classes (int): Number of classes in the dataset. Defaults to 10.
+            hidden_size (Union[int, List]): The number of `neurons` in each hidden layer. Defaults to 128.
+            num_layers (int): The number of hidden layers. Defaults to 3.
+            dropout_rate (float): The dropout rate at each layer. Defaults to 0.2.
+            activation_fn (str): Name of the activation function in the hidden layers. Defaults to
+                relu.
+
+        """
+        super().__init__()
+
+        activation_fn = activation_function(activation_fn)
+
+        if isinstance(hidden_size, int):
+            hidden_size = [hidden_size] * num_layers
+
+        self.layers = [
+            Rearrange("b c h w -> b (c h w)"),
+            nn.Linear(in_features=input_size, out_features=hidden_size[0]),
+            activation_fn,
+        ]
+
+        for i in range(num_layers - 1):
+            self.layers += [
+                nn.Linear(in_features=hidden_size[i], out_features=hidden_size[i + 1]),
+                activation_fn,
+            ]
+
+            if dropout_rate:
+                self.layers.append(nn.Dropout(p=dropout_rate))
+
+        self.layers.append(
+            nn.Linear(in_features=hidden_size[-1], out_features=num_classes)
+        )
+
+        self.layers = nn.Sequential(*self.layers)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """The feedforward pass."""
+        # If batch dimenstion is missing, it needs to be added.
+        if len(x.shape) < 4:
+            x = x[(None,) * (4 - len(x.shape))]
+        return self.layers(x)
+
+    @property
+    def __name__(self) -> str:
+        """Returns the name of the network."""
+        return "mlp"