Detection of Arrows in On-Line Sketched Diagrams Using Relative Stroke Positioning

This paper deals with recognition of arrows in online sketched diagrams. Arrows have varying appearance and thus it is a difficult task to recognize them directly. It is beneficial to detect arrows after other symbols (easier to detect) are already found. We proposed [4] an arrow detector which searches for arrows as arbitrarily shaped connectors between already found symbols. The detection is done two steps: a) a search for a shaft of the arrow, b) a search for its head. The first step is relatively easy. However, it might be quite difficult to find the head reliably. This paper brings two contributions. The first contribution is a design of an arrow recognizer where the head is detected using relative strokes positioning. We embedded this recognizer into the diagram recognition pipeline proposed earlier [4] and increased the overall accuracy. The second contribution is an introduction of a new approach to evaluate the relative position of two given strokes with neural networks (LSTM). This approach is an alternative to the fuzzy relative positioning proposed by Bout ruche et al. [2]. We made a comparison between the two methods through experiments performed on two datasets for two different tasks. First, we used a benchmark database of hand-drawn finite automata to evaluate detection of arrows. Second, we used a database presented in the paper by Bout ruche et al. containing pairs of reference and argument strokes, where argument strokes are classified into 18 classes. Our method gave significantly better results for the first task and comparable results for the second task.