Evaluation of a traceability approach for informal freehand sketches

Kleffmann, Markus; Röhl, Sebastian; Book, Matthias; Gruhn, Volker

doi:10.1007/s10515-017-0221-6

Cited by 4 publications

(1 citation statement)

References 83 publications

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“…Kleffmann et al [27] developed a traceability approach for recognizing the informal hand-drawn sketches by creating the relationships between sketch elements.…”

Section: Related Workmentioning

confidence: 99%

SKETRACK: Stroke-Based Recognition of Online Hand-Drawn Sketches of Arrow-Connected Diagrams and Digital Logic Circuit Diagrams

Altun

Nooruldeen

2019

Scientific Programming

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Digitalization of handwritten documents has created a greater need for accurate online recognition of hand-drawn sketches. However, the online recognition of hand-drawn diagrams is an enduring challenge in human-computer interaction due to the complexity in extracting and recognizing the visual objects reliably from a continuous stroke stream. This paper focuses on the design and development of a new, efficient stroke-based online hand-drawn sketch recognition scheme named SKETRACK for hand-drawn arrow diagrams and digital logic circuit diagrams. The fundamental parts of this model are text separation, symbol segmentation, feature extraction, classification, and structural analysis. The proposed scheme utilizes the concepts of normalization and segmentation to isolate the text from the sketches. Then, the features are extracted to model different structural variations of the strokes that are categorized into the arrows/lines and the symbols for effective processing. The strokes are clustered using the spectral clustering algorithm based on p-distance and Euclidean distance to compute the similarity between the features and minimize the feature dimensionality by grouping similar features. Then, the symbol recognition is performed using modified support vector machine (MSVM) classifier in which a hybrid kernel function with a lion optimized tuning parameter of SVM is utilized. Structural analysis is performed with lion-based task optimization for recognizing the symbol candidates to form the final diagram representations. This proposed recognition model is suitable for simpler structures such as flowcharts, finite automata, and the logic circuit diagrams. Through the experiments, the performance of the proposed SKETRACK scheme is evaluated on three domains of databases and the results are compared with the state-of-the-art methods to validate its superior efficiency.

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“…Kleffmann et al [27] developed a traceability approach for recognizing the informal hand-drawn sketches by creating the relationships between sketch elements.…”

Section: Related Workmentioning

confidence: 99%

SKETRACK: Stroke-Based Recognition of Online Hand-Drawn Sketches of Arrow-Connected Diagrams and Digital Logic Circuit Diagrams

Altun

Nooruldeen

2019

Scientific Programming

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Relation Artefacts Type II

Clemmensen¹

2021

Human Work Interaction Design

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AugIR Meets GestureCards: A Digital Sketching Environment for Gesture-Based Applications

Hesenius

Kleffmann²,

Gruhn

2021

Interacting With Computers

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To gain a common understanding of an application’s layouts, dialogs and interaction flows, development teams often sketch user interface (UI). Nowadays, they must also define multi-touch gestures, but tools for sketching UIs often lack support for custom gestures and typically just integrate a basic predefined gesture set, which might not suffice to specifically tailor the interaction to the desired use cases. Furthermore, sketching can be enhanced with digital means, but it remains unclear whether digital sketching is actually beneficial when designing gesture-based applications. We extended the AugIR, a digital sketching environment, with GestureCards, a hybrid gesture notation, to allow software engineers to define custom gestures when sketching UIs. We evaluated our approach in a user study contrasting digital and analog sketching of gesture-based UIs.

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Evaluation of a traceability approach for informal freehand sketches

Cited by 4 publications

References 83 publications

SKETRACK: Stroke-Based Recognition of Online Hand-Drawn Sketches of Arrow-Connected Diagrams and Digital Logic Circuit Diagrams

SKETRACK: Stroke-Based Recognition of Online Hand-Drawn Sketches of Arrow-Connected Diagrams and Digital Logic Circuit Diagrams

Relation Artefacts Type II

AugIR Meets GestureCards: A Digital Sketching Environment for Gesture-Based Applications

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