Online handwriting recognition is challenging but an already well-studied topic. However, recent advances in the development of convolutional neural networks (CNN) make us believe that these networks could still improve the state of the art especially in the much more challenging context of online children handwritten letters recognition. This is because, children handwriting is, at an early stage of learning, approximate and includes deformed letters. To evaluate the potential of these networks, we study the early and late fusions of different input channels that can provide a CNN with information about the handwriting dynamics in addition to the static image of the characters. The experiments on a real children handwriting dataset with 27 000 characters acquired in primary schools, show that using multiple channels with CNN, improves the accuracy performance of different CNN architectures and different fusion settings for character recognition.