Online flowchart understanding by combining max-margin Markov random field with grammatical analysis

Abstract : Flowcharts are considered in this work as a specific 2D handwritten language where the basic strokes are the terminal symbols of a graphical language governed by a 2D grammar. In this way, they can be regarded as structured objects, and we propose to use a MRF to model them, and to allow assigning a label to each of the strokes. We use structured SVM as learning algorithm, maximizing the margin between true labels and incorrect labels. The model would automatically learn the implicit grammatical information encoded among strokes, which greatly improves the stroke labeling accuracy compared to previous researches that incorporated human prior knowledge of flowchart structure. We further complete the recognition by using grammatical analysis, which finally brings coherence to the whole flowchart recognition by labeling the relations between the detected objects.
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International Journal on Document Analysis and Recognition, Springer Verlag, 2017, 20 (2), pp.123-136. 〈10.1007/s10032-017-0284-8〉
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Chengcheng Wang, Harold Mouchère, Aurélie Lemaitre, Christian Viard-Gaudin. Online flowchart understanding by combining max-margin Markov random field with grammatical analysis. International Journal on Document Analysis and Recognition, Springer Verlag, 2017, 20 (2), pp.123-136. 〈10.1007/s10032-017-0284-8〉. 〈hal-01503366〉

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