In this thesis the verification of a neural network’s proficiency at labeling ionizing radiation particles from the unsorted output of a timepix3 camera is attempted. Focus is put on labeling single particles in separate data sequences with slightly preprocessed input data. Preprocessing of input data is done to simplify the patterns that should be recognized. Two major choices were available for this project, Elman-network and Jordan-network. A more complicated type was not an option because of the longer time needed to implement them. The network type chosen was Elman because of freedom in context size. The neural network is created and trained with the TensorFlow API in python with labeled data that was not created by hand. The network recognized the length difference between gamma particles and alpha particles. Beta particles were not considered by the network. It is concluded that the Elman-style network is not proficient in labeling the sequences, which were considered short enough and to have simple enough input data. A more modern network type is therefore likely required to solve this problem.

Optical character recognition (OCR) has been a topic of interest for many years. It is defined as the process of digitizing a document image into a sequence of characters. Despite decades of intense research, OCR systems with capabilities to that of human still remains an open challenge. In this work there is presented a design and implementation of such system, which is capable of detecting texts in graphical user interfaces.