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diff --git a/text_recognizer/networks/lenet.py b/text_recognizer/networks/lenet.py
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+"""Implementation of the LeNet network."""
+from typing import Callable, Dict, Optional, Tuple
+
+from einops.layers.torch import Rearrange
+import torch
+from torch import nn
+
+from text_recognizer.networks.util import activation_function
+
+
+class LeNet(nn.Module):
+    """LeNet network for character prediction."""
+
+    def __init__(
+        self,
+        channels: Tuple[int, ...] = (1, 32, 64),
+        kernel_sizes: Tuple[int, ...] = (3, 3, 2),
+        hidden_size: Tuple[int, ...] = (9216, 128),
+        dropout_rate: float = 0.2,
+        num_classes: int = 10,
+        activation_fn: Optional[str] = "relu",
+    ) -> None:
+        """Initialization of the LeNet network.
+
+        Args:
+            channels (Tuple[int, ...]): Channels in the convolutional layers. Defaults to (1, 32, 64).
+            kernel_sizes (Tuple[int, ...]): Kernel sizes in the convolutional layers. Defaults to (3, 3, 2).
+            hidden_size (Tuple[int, ...]): Size of the flattend output form the convolutional layers.
+                Defaults to (9216, 128).
+            dropout_rate (float): The dropout rate. Defaults to 0.2.
+            num_classes (int): Number of classes. Defaults to 10.
+            activation_fn (Optional[str]): The name of non-linear activation function. Defaults to relu.
+
+        """
+        super().__init__()
+
+        activation_fn = activation_function(activation_fn)
+
+        self.layers = [
+            nn.Conv2d(
+                in_channels=channels[0],
+                out_channels=channels[1],
+                kernel_size=kernel_sizes[0],
+            ),
+            activation_fn,
+            nn.Conv2d(
+                in_channels=channels[1],
+                out_channels=channels[2],
+                kernel_size=kernel_sizes[1],
+            ),
+            activation_fn,
+            nn.MaxPool2d(kernel_sizes[2]),
+            nn.Dropout(p=dropout_rate),
+            Rearrange("b c h w -> b (c h w)"),
+            nn.Linear(in_features=hidden_size[0], out_features=hidden_size[1]),
+            activation_fn,
+            nn.Dropout(p=dropout_rate),
+            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)