- Most commonly used to recognize handwritten and machine printed characters, e.g., recognizing the handwritten numbers on bank checks.

University of Michigan - Ann Arbor

- LeNet-5 is trained on 32 x 32 Gray-scale images.
- The first layer has six, 5 x 5 filters with a stride of one. The output is passed to a 2 x 2 average pooling with a stride of two.
- The second layer has 16, 5 x 5 filters that pass the results to a 2 x 2 average pooling with a stride of two.
- Two fully connected layers get the output and pass it to a softmax output layer to detect one of ten classes.

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