The use of hand-drawn gestures for text editing

Wolf, Catherine G.; Morrel-Samuels, Palmer

doi:10.1016/s0020-7373(87)80045-7

Cited by 47 publications

(17 citation statements)

References 5 publications

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“…Such editing tasks consist of two parts: selecting a command and indicating the scope of the command. Previous research suggests that pen gestures are intuitive and efficient for such command control tasks [Rhyne and Wolf 1993;Wolf and Morrel-Samuels 1987]. Figure 4 illustrates the pen gestures used in our multimodal dictation system, including gestures for deleting, positioning the cursor, and selecting input items at different input levels (phrases, words, and characters within a word).…”

Section: Editing Using Pen-based Gesturesmentioning

confidence: 99%

Multimodal error correction for speech user interfaces

Suhm¹,

Myers

Waibel

2001

ACM Trans. Comput.-Hum. Interact.

167

125

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Although commercial dictation systems and speech-enabled telephone voice user interfaces have become readily available, speech recognition errors remain a serious problem in the design and implementation of speech user interfaces. Previous work hypothesized that switching modality could speed up interactive correction of recognition errors. This article presents multimodal error correction methods that allow the user to correct recognition errors efficiently without keyboard input. Correction accuracy is maximized by novel recognition algorithms that use context information for recognizing correction input. Multimodal error correction is evaluated in the context of a prototype multimodal dictation system. The study shows that unimodal repair is less accurate than multimodal error correction. On a dictation task, multimodal correction is faster than unimodal correction by respeaking. The study also provides empirical evidence that system-initiated error correction (based on confidence measures) may not expedite error correction. Furthermore, the study suggests that recognition accuracy determines user choice between modalities: while users initially prefer speech, they learn to avoid ineffective correction modalities with experience. To extrapolate results from this user study, the article introduces a performance model of (recognition-based) multimodal interaction that predicts input speed including time needed for error correction. Applied to interactive error correction, the model predicts the impact of improvements in recognition technology on correction speeds, and the influence of recognition accuracy and correction method on the productivity of dictation systems. This model is a first step toward formalizing multimodal interaction.

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Section: Editing Using Pen-based Gesturesmentioning

confidence: 99%

Multimodal error correction for speech user interfaces

Suhm¹,

Myers

Waibel

2001

ACM Trans. Comput.-Hum. Interact.

167

125

View full text Add to dashboard Cite

show abstract

“…One of the first studies of hand-drawn gestures for simple editing tasks dates back to 1987, when Wolf and Morrel-Samuels (1987) reported that "the use of gestures is of particular interest in an interface which allows the user to write directly on the surface of a display with a stylus." Simple gestures led to good intra-subject consistency and there was consensus regarding gestures being perceived as easy to use and remember.…”

Section: Related Workmentioning

confidence: 99%

Context-Aware Gestures for Mixed-Initiative Text Editing UIs

et al. 2014

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Oxford University PressLeiva, LA.; Alabau, V.; Romero Gómez, V.; Toselli, AH.; Vidal, E. (2015). Contex-aware gestures for mixed-initiative text editings UIs. Interacting with Computers. 27(6):675-696. doi:10.1093/iwc/iwu019. +34 963878172 Email: {luileito,valabau,vromero,ahector,evidal}@prhlt.upv.es This work is focused on enhancing highly interactive text-editing applications with gestures. Concretely, we study CATTI, a handwriting transcription system that follows a corrective feedback paradigm, where both the user and the system collaborate efficiently to produce a high-quality text transcription. CATTI-like applications demand fast and accurate gesture recognition, for which we observed that current gesture recognizers are not adequate enough. In response to this need we developed MinGestures, a parametric context-aware gesture recognizer. Our contributions include a number of stroke features for disambiguating copy-mark gestures from handwritten text, plus the integration of these gestures in a CATTI application. It becomes finally possible to create highly interactive stroke-based textediting interfaces, without worrying to verify the user intent onscreen.We performed a formal evaluation with 22 e-pen users and 32 mouse users using a gesture vocabulary of 10 symbols. MinGestures achieved an outstanding accuracy (less than 1% error rate) with very high performance (less than 1 ms of recognition times). We then integrated MinGestures in a CATTI prototype and tested the performance of the interactive handwriting system when it is driven by gestures. Our results show that using gestures in interactive handwriting applications is both advantageous and convenient when gestures are simple but context-aware. Taken together, this work suggests that text-editing interfaces not only can be easily augmented with simple gestures, but also may substantially improve user productivity.

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“…Some previous reCopyright c 2011 The Institute of Electronics, Information and Communication Engineers searches shows that gestures are natural for humans, and only a short time of training is required before people can consistently use new gestures to communicate information or control devices [1]- [3].…”

Section: Related Workmentioning

confidence: 99%

A Recognition Method for One-Stroke Finger Gestures Using a MEMS 3D Accelerometer

Lei

Zhou

Cheng

et al. 2011

IEICE Trans. Inf. & Syst.

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SUMMARYAutomatic recognition of finger gestures can be used for promotion of life quality. For example, a senior citizen can control the home appliance, call for help in emergency, or even communicate with others through simple finger gestures. Here, we focus on one-stroke finger gesture, which are intuitive to be remembered and performed. In this paper, we proposed and evaluated an accelerometer-based method for detecting the predefined one-stroke finger gestures from the data collected using a MEMS 3D accelerometer worn on the index finger. As alternative to the optoelectronic, sonic and ultrasonic approaches, the accelerometerbased method is featured as self-contained, cost-effective, and can be used in noisy or private space. A compact wireless sensing mote integrated with the accelerometer, called MagicRing, is developed to be worn on the finger for real data collection. A general definition on one-stroke gesture is given out, and 12 kinds of one-stroke finger gestures are selected from human daily activities. A set of features is extracted among the candidate feature set including both traditional features like standard deviation, energy, entropy, and frequency of acceleration and a new type of feature called relative feature. Both subject-independent and subject-dependent experiment methods were evaluated on three kinds of representative classifiers. In the subject-independent experiment among 20 subjects, the decision tree classifier shows the best performance recognizing the finger gestures with an average accuracy rate for 86.92 %. In the subject-dependent experiment, the nearest neighbor classifier got the highest accuracy rate for 97.55 %.

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The use of hand-drawn gestures for text editing

Cited by 47 publications

References 5 publications

Multimodal error correction for speech user interfaces

Multimodal error correction for speech user interfaces

Context-Aware Gestures for Mixed-Initiative Text Editing UIs

A Recognition Method for One-Stroke Finger Gestures Using a MEMS 3D Accelerometer

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