24 January 2011 First experiments on a new online handwritten flowchart database
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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.
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Ahmad-Montaser Awal, Ahmad-Montaser Awal, Guihuan Feng, Guihuan Feng, Harold Mouchère, Harold Mouchère, Christian Viard-Gaudin, Christian Viard-Gaudin, } "First experiments on a new online handwritten flowchart database", Proc. SPIE 7874, Document Recognition and Retrieval XVIII, 78740A (24 January 2011); doi: 10.1117/12.876624; https://doi.org/10.1117/12.876624

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