  Finding the name of an unknown symbol is often hard, but writing the symbol
is easy. This bachelor's thesis presents multiple systems that use the pen
trajectory to classify handwritten symbols. Five preprocessing steps, one data
augmentation algorithm, five features and five variants for multilayer
Perceptron training were evaluated using 166898 recordings which were collected
with two crowdsourcing projects. The evaluation results of these 21 experiments
were used to create an optimized recognizer which has a TOP1 error of less than
17.5% and a TOP3 error of 4.0%. This is an improvement of 18.5% for the TOP1
error and 29.7% for the TOP3 error.
