We propose in this paper a new online handwritten flowchart database and perform some first experiments to have a
baseline benchmark on this dataset. The collected database consists of 419 flowcharts labeled at the stroke and symbol
levels. In addition, an isolated database of graphical and text symbols was extracted from these collected flowcharts.
Then, we tackle the problem of online handwritten flowchart recognition from two different points of view. Firstly, we
consider that flowcharts are correctly segmented, and we propose different classifiers to perform two tasks, text/non-text
separation and graphical symbol recognition. Tested with the extracted isolated test database, we achieve up to 90% and
98% in text/non-text separation and up to 93.5% in graphical symbols recognition. Secondly, we propose a global
approach to perform flowchart segmentation and recognition. For this latter, we adopt a global learning schema and a
recognition architecture that considers a simultaneous segmentation and recognition. Global architecture is trained and
tested directly with flowcharts. Results show the interest of such global approach, but regarding the complexity of
flowchart segmentation problem, there is still lot of space to improve the global learning and recognition methods.