On-line Recognition of Handwritten Mathematical Symbols
This work addresses the challenge of symbol recognition for users in mathematical and educational contexts, but it is incremental as it builds on existing methods with optimizations.
The paper tackles the problem of recognizing handwritten mathematical symbols from pen trajectories, achieving a TOP1 error of less than 17.5% and a TOP3 error of 4.0%, which represents improvements of 18.5% and 29.7% respectively.
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.