Sketch Recognition Based Intelligent Whiteboard Teaching System

Yuan, Zhenming; Jin, Guichao

doi:10.1109/csse.2008.123

Cited by 2 publications

(2 citation statements)

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“…Some methods depends on linear least squares fitting to a conic section equation, so it can identify sketch strokes, lines, elliptic arcs, and corners [6]. Other methods depend on Fuzzy logic, neural networks or SVM are used to classify the shapes to one of a set of specified classes [11][13] [14]. There is another important problem despite the used technique or method.…”

Section: Shape Recognitionmentioning

confidence: 99%

“…al developed a method for the recognition of on-line handwritten commutative diagrams. Diagrams are formed with arrows that join relatively simple mathematical expressions [1].Zhenming and Guichao presented a Sketch Recognition based Intelligent Whiteboard Teaching System [14]. Their system can recognize the gestures to help users modify, copy and move sketches as familiar pencil-and-paper process, and transform the freehand sketches into the symbols of the domain-specific knowledge.…”

Section: Introductionmentioning

confidence: 99%

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Recognition of On-line Hand Sketched Complex Diagrams

Al-Marakeby¹

2016

International Journal of Advanced Research in Computer and Comm

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The spread and availability of pen-based devices increased the research in free handwriting and hand sketched recognition. Most of systems in hand sketched diagram recognition are limited to a specific application or limited to specific shapes. Complex diagrams recognition requires not only understanding the primitive shapes but also determining relations between these shapes. This work presents a system for recognition of hand-sketched complex diagrams which create active and intelligent relations between different diagram components. A multi-layers framework is developed which consists of stroke layer, shape layer, and diagram layer. A distance function is used to measure the similarity between template shapes and user sketched shapes. In the diagram layer, many adjustments are applied to enhance the positions and sizes of shapes, increasing the harmony and regularity of shapes arrangement. This system has been tested using a set of primitive shapes and a set of complex diagram and high accuracy results have been obtained.

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Section: Shape Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%