Recognition and beautification of multi-stroke symbols in digital ink

Hse, Heloise Hwawen; Newton, A.R.

doi:10.1016/j.cag.2005.05.006

Cited by 40 publications

(13 citation statements)

References 24 publications

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“…The system figures the mistake concerning the coveted class utilizing back spread calculation. Existing sketch recognition systems can be generally partitioned into three categories: stroke-based [6] - [8], rule-based [1,2], [3] and feature-based [9], [10]. Stroke-based recognition is based around the preface that each stroke has a specific role in representing a sketch.…”

Section: Introductionmentioning

confidence: 99%

KNN based Hand Drawn Electrical Circuit Recognition

Dewangan¹

2018

IJRASET

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Electrical diagram is foundation in electrical science. A circuit diagram pass on numerous data about the framework. Behind any gadget there are a lot of electrical fixings which play out their particular tasks, today all the electrical programming instruments neglected to successfully change over the data naturally from a circuit picture diagram to computerized shape. Henceforth electrical designers ought to physically enter all data into PCs, and this procedure requires some investment and carry mistakes with high likelihood. Besides, when the diagram is hand drawn, the issue is more convoluted for any electrical investigation. Accordingly, in this paper we propose another strategy utilizing K-Nearest Neighbour (KNN) to make a machine that can straightforwardly read the electrical images from a hand drawn circuit picture.

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Section: Introductionmentioning

confidence: 99%

KNN based Hand Drawn Electrical Circuit Recognition

Dewangan¹

2018

IJRASET

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“…Most have been tested solely on isolated primitive shapes and rely on special button presses [Hse and Newton 2005;Calhoun et al 2002] or unspecified timeouts [Fonseca and Jorge 2000] to group the strokes that belong to a single primitive. These types of approaches completely disallow stroke interspersing.…”

Section: Comparisons To Other Methodsmentioning

confidence: 99%

“…When performing template matching, many gesture algorithms will use a simple path-based Euclidean distance metric, while the appearance-based may use point-based distance metrics like the Hausdorff distance [Kara and Stahovich 2004;Wolin et al 2009]. Hse and Newton performed recognition using Zernike moments [Hse and Newton 2005]. Oltmans used shape context to match shapes against a bullseye-shaped template [Oltmans 2007].…”

Section: Appearance-based Algorithmsmentioning

confidence: 99%

Recognizing sketched multistroke primitives

Hammond

Paulson

2011

ACM Trans. Interact. Intell. Syst.

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Sketch recognition attempts to interpret the hand-sketched markings made by users on an electronic medium. Through recognition, sketches and diagrams can be interpreted and sent to simulators or other meaningful analyzers. Primitives are the basic building block shapes used by high-level visual grammars to describe the symbols of a given sketch domain. However, one limitation of these primitive recognizers is that they often only support basic shapes drawn with a single stroke. Furthermore, recognizers that do support multistroke primitives place additional constraints on users, such as temporal timeouts or modal button presses to signal shape completion. The goal of this research is twofold. First, we wanted to determine the drawing habits of most users. Our studies found multistroke primitives to be more prevalent than multiple primitives drawn in a single stroke. Additionally, our studies confirmed that threading is less frequent when there are more sides to a figure. Next, we developed an algorithm that is capable of recognizing multistroke primitives without requiring special drawing constraints. The algorithm uses a graph-building and search technique that takes advantage of Tarjan's linear search algorithm, along with principles to determine the goodness of a fit. Our novel, constraint-free recognizer achieves accuracy rates of 96% on freely-drawn primitives.

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“…However, due to the imprecise nature of contact points on the screen, the user's ink may not be rendered where they intend. Therefore we beautify the ink to try to produce what the user intends, in a similar way to many digital ink drawing programs such as [32]. Our system includes two forms of ink beautification: ink to tangible edge snapping and ink corner snapping.…”

Section: Ink Beautificationmentioning

confidence: 99%

CapTUI: Geometric Drawing with Tangibles on a Capacitive Multi-touch Display

Blagojevic

Plimmer

2013

Human-Computer Interaction – INTERACT 2013

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Abstract. We present CapTUI, an innovative drawing tool that detects tangible drawing instruments on a capacitive multi-touch tablet. There are three core components to the system: the tangible hardware, the recognizer used to identify the tangibles and the drawing software that works in tandem with the tangibles to provide intelligent visual drawing guides. Our recognizable tangible drawing instruments are a ruler, protractor and set square. Users employ these familiar physical instruments to construct digital ink drawings on a tablet in an intuitive and engaging manner. The visual drawing guides enhance the experience by offering the user helpful cues and functionalities to assist them to draw more accurately. A user evaluation comparing CapTUI to an application with passive tools showed that users significantly preferred CapTUI and found that the visual guides provide greater accuracy when drawing.

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Recognition and beautification of multi-stroke symbols in digital ink

Cited by 40 publications

References 24 publications

KNN based Hand Drawn Electrical Circuit Recognition

KNN based Hand Drawn Electrical Circuit Recognition

Recognizing sketched multistroke primitives

CapTUI: Geometric Drawing with Tangibles on a Capacitive Multi-touch Display

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