Overlapped Handwriting Input on Mobile Phones

Zou, Yanming; Liu, Yingfei; Liu, Ying; Wang, Kongqiao

doi:10.1109/icdar.2011.82

Cited by 12 publications

(4 citation statements)

References 6 publications

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“…However, there are situations where it is too noisy or inappropriate to use; e.g., asking for personal data on an overcrowded environment. Researchers have proposed to use handwriting to enter text on mobile devices [17,45], though it is difficult for the user to see what is currently being written on very small screens. In addition, handwritten text (much like voice) is prone to recognition errors.…”

Section: Related Workmentioning

confidence: 99%

Text Entry on Tiny QWERTY Soft Keyboards

Leiva

Sahami

Catalá

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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The advent of wearables (e.g., smartwatches, smartglasses, and digital jewelry) anticipates the need for text entry methods on very small devices. We conduct fundamental research on this topic using 3 qwerty-based soft keyboards for 3 different screen sizes, motivated by the extensive training that users have with qwerty keyboards. In addition to ZoomBoard (a soft keyboard for diminutive screens), we propose a calloutbased soft keyboard and ZShift, a novel extension of the Shift pointing technique. We conducted a comprehensive user study followed by extensive analyses on performance, usability, and short-term learning. Our results show that different small screen sizes demand different types of assistance. In general, manufacturers can benefit from these findings by selecting an appropriate qwerty soft keyboard for their devices. Ultimately, this work provides designers, researchers, and practitioners with new understanding of qwerty soft keyboard design space and its scalability for tiny touchscreens.

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Section: Related Workmentioning

confidence: 99%

Text Entry on Tiny QWERTY Soft Keyboards

Leiva

Sahami

Catalá

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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“…또한 스마트 폰의 문자 입력 장치를 정리 하고 이들의 사용성을 비교하는 연구도 진행되었다 (Page, 2013). 이 외에도 자판을 사용하지 않고 손글씨를 직접 입력하면 문 자로 전환시키는 손글씨 인식 문자입력도구도 개발되었다 (Zou et al, 2011;Kienzle and Hinckley, 2013 Note) For significant F ratios, bold font indicates which application showed higher entry speed.…”

Section: 연구 배경unclassified

Comparison of Soft Keyboard Types for Stylus Pen and Finger-based Interaction on Tablet PCs

Ahn

Ahn²,

Lee³

et al. 2016

Journal of Korean Institute of Industrial Engineers

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Pen-based interaction is universally available on smart devices and especially on Tablet PCs. Previous studies compared various input methods like fingers, a mouse or a stylus pen on PCs or on a touchscreen based devices such as smart phones. At the same time, various soft keyboard applications are being developed on application stores of smart devices. However, these previous studies did not suggest which one is a suitable keyboard application for Tablet PCs when users perform a certain interaction as input type. In this study, we compared two types of input methods (finger and pen) and three types of soft keyboard applications (QWERTY, Gesture and Swype) in a Tablet PC using performance measurements (accuracy and input speed) and discussed what types of applications showed better performance with each interaction on tablet PC. From these results, recommendations for the keyboard types depending on the input methods on tablet PCs were developed. †

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“…To input overlaid Chinese handwriting, Zou et al [10] proposed a quick segmentation method based on an artificial neural network to detect the first stroke of each character, and then let previous characters fade out for the clear viewing of the current character. The quick segmentation method is also used in the recognition system to speed up the whole recognition process.…”

Section: Copyright Cmentioning

confidence: 99%

Character-Position-Free On-Line Handwritten Japanese Text Recognition by Two Segmentation Methods

Liang

Zhu

Kumagai

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARY The paper presents a recognition method of characterposition-free on-line handwritten Japanese text patterns to allow a user to overlay characters freely without confirming previously written characters. To develop this method, we first collected text patterns written without wrist or elbow support and without visual feedback and then prepared large sets of character-position-free handwritten Japanese text patterns artificially from normally handwritten text patterns. The proposed method sets each off-stroke between real strokes as undecided and evaluates the segmentation probability by SVM model. Then, the optimal segmentationrecognition path can be effectively found by Viterbi search in the candidate lattice, combining the scores of character recognition, geometric features, linguistic context, as well as the segmentation scores by SVM classification. We test this method on variously overlaid sample patterns, as well as on the above-mentioned collected handwritten patterns, and verify that its recognition rates match those of the latest recognizer for normally handwritten horizontal Japanese text with no serious speed restriction in practical applications.

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Overlapped Handwriting Input on Mobile Phones

Cited by 12 publications

References 6 publications

Text Entry on Tiny QWERTY Soft Keyboards

Text Entry on Tiny QWERTY Soft Keyboards

Comparison of Soft Keyboard Types for Stylus Pen and Finger-based Interaction on Tablet PCs

Character-Position-Free On-Line Handwritten Japanese Text Recognition by Two Segmentation Methods

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