Using YOLO Network for Automatic Processing of Finite Automata Images with Application to Bit-Strings Recognition

The recognition of handwritten diagrams has drawn attention in recent years because of their potential applications in many areas, especially when it can be used for educational purposes. Although there are many online approaches, the advances of deep object detector networks have made offline recognition an attractive option, allowing simple inputs such as paper-drawn diagrams. In this paper, we have tested the YOLO network, including its version with fewer parameters, YOLO-Tiny, for the recognition of images of finite automata. This recognition was applied to the development of an application that recognizes bit-strings used as input to the automaton: given an image of a transition diagram, the user inserts a sequence of bits and the system analyzes whether the automaton recognizes the sequence or not. Using two bases of finite automata, we have evaluated the detection and recognition of finite automata symbols as well as bit-string processing. With regard to the diagram symbol detection task, experiments on a handwritten finite automata image dataset returned 82.04% and 97.20% for average precision and recall, respectively.