First experiments on a new online handwritten flowchart database

Awal, Ahmad-Montaser; Feng, Guorui; Mouchère, Harold; Viard-Gaudin, Christian

doi:10.1117/12.876624

Cited by 48 publications

(41 citation statements)

References 11 publications

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“…We have also compared our results with the ones proposed by Awal et al in [5], who use a statistical approach. The results in table III demonstrate the interest of the syntactic and structural knowledge for flowchart recognition.…”

Section: B First Results and Discussionmentioning

confidence: 79%

“…In order to evaluate our method, we have worked on the freely available database presented by Awal et al in [5].…”

Section: A Database and Metricsmentioning

confidence: 99%

“…Thus, Yuan et al [4] use a hybrid SVM-HMM for sketch recognition. Awal et al [5] have also presented some work for flowchart recognition. Concerning the recognition of symbols, they apply two different methods: the separation of text and graphic symbols using a method based on entropy, and the recognition of symbols using TDNN or SVM after a step of stroke re-ordering.…”

Section: Introductionmentioning

confidence: 99%

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Interest of Syntactic Knowledge for On-Line Flowchart Recognition

Lemaitre

Mouchère

Camillerapp

et al. 2013

Graphics Recognition. New Trends and Challenges

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Abstract-In this paper, we address the problem of segmentation and recognition of on-line flowcharts. Flowcharts are bi-dimensional documents, in the sense that the order of writing is not defined. Some statistical approaches have been proposed in the literature to label and segment the flowcharts. However, as they are very well structured documents, we propose to introduce some structural and syntactic knowledge on flowcharts to improve their recognition. For this purpose, we have used an existing grammatical off-line method with on-line a posteriori signal. We apply this work on a freely available database. The results demonstrate the interest of structural knowledge on the context to improve the recognition.

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Section: B First Results and Discussionmentioning

confidence: 79%

“…In order to evaluate our method, we have worked on the freely available database presented by Awal et al in [5].…”

Section: A Database and Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Interest of Syntactic Knowledge for On-Line Flowchart Recognition

Lemaitre

Mouchère

Camillerapp

et al. 2013

Graphics Recognition. New Trends and Challenges

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“…There are no public flowchart or any other sketch diagram dataset until 2011 [2], and after that, many researches appeared to improve that first trial research. Lemaitre et.…”

Section: D Handwritten Language Recognitionmentioning

confidence: 99%

“…After stroke labeling, we use a grammatical description language extended from a previous work [18] to parse the structure of a given sketched diagram, thus obtaining the final recognized flowchart. In order to fully evaluate of the recognition process, the ground-truth of the flowchart dataset [2] have been completed by adding the object relations.…”

mentioning

confidence: 99%

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

Wang

Mouchère

Lemaitre

et al. 2017

IJDAR

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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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Online Handwritten Diagram Recognition with Graph Attention Networks

Yun

Zhang

et al. 2019

Lecture Notes in Computer Science

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First experiments on a new online handwritten flowchart database

Cited by 48 publications

References 11 publications

Interest of Syntactic Knowledge for On-Line Flowchart Recognition

Interest of Syntactic Knowledge for On-Line Flowchart Recognition

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

Online Handwritten Diagram Recognition with Graph Attention Networks

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