A generic approach for desining on-line handwritten shapes recognizers

This paper presents a generic approach for designing on-line handwritten shapes recognizers. Our approach allows designing very different recognition engines that correspond to various needs in pen-based interfaces. In particular, it allows dealing with a wide class of symbols and characters. We present in detail our system and make the link between our models and more standard statistical models such as Hierarchical Hidden Markov Models and Dynamic Bayesian Networks. We then evaluate fundamental properties of our approach: learning from scratch any symbol, learning from very few training sample. We show experimentally that, using our approach, one can learn both a state-of-the-art writerindependent recognizer for alphanumeric characters, and a writer-dependent recognizer working with any twodimensional shapes that learns a new symbol with a few training samples and requires very few machines resources.Dans ce papier, nous présentons une approche générique pour le développement de moteurs de reconnaissance de symboles manuscrits en ligne. Cette approche permet de concevoir des systèmes de reconnaissance de types très variés correspondant à différents contextes des interfaces stylo, pouvant notamment fonctionner sur diverses classes de caractères ou symboles. Nous présentons en détail notre approche et faisons le lien avec d’une part les modèles de Markov hiérarchiques et d’autre part les réseaux bayésiens dynamiques. Nous évaluons ensuite les propriétés fondamentales de notre approche qui lui confèrent une grande flexibilité. Puis nous montrons que l’on peut, avec cette approche générique, concevoir aussi bien des systèmes omni-scripteur rivalisant avec les meilleurs systèmes actuels sur des caractères alphanumériques usuels, que des systèmes mono-scripteur pour des symboles graphiques quelconques, nécessitant très peu d’exemples d’apprentissage et peu gourmands en ressources machine

Enabling mobile microinteractions

While much attention has been paid to the usability of desktop computers, mobile com- puters are quickly becoming the dominant platform. Because mobile computers may be used in nearly any situation--including while the user is actually in motion, or performing other tasks--interfaces designed for stationary use may be inappropriate, and alternative interfaces should be considered. In this dissertation I consider the idea of microinteractions--interactions with a device that take less than four seconds to initiate and complete. Microinteractions are desirable because they may minimize interruption; that is, they allow for a tiny burst of interaction with a device so that the user can quickly return to the task at hand. My research concentrates on methods for applying microinteractions through wrist- based interaction. I consider two modalities for this interaction: touchscreens and motion- based gestures. In the case of touchscreens, I consider the interface implications of making touchscreen watches usable with the finger, instead of the usual stylus, and investigate users' performance with a round touchscreen. For gesture-based interaction, I present a tool, MAGIC, for designing gesture-based interactive system, and detail the evaluation of the tool.Ph.D.Committee Chair: Starner, Thad; Committee Member: Abowd, Gregory; Committee Member: Isbell, Charles; Committee Member: Landay, james; Committee Member: McIntyre, Blai

Discoverable Free Space Gesture Sets for Walk-Up-and-Use Interactions

abstract: Advances in technology are fueling a movement toward ubiquity for beyond-the-desktop systems. Novel interaction modalities, such as free space or full body gestures are becoming more common, as demonstrated by the rise of systems such as the Microsoft Kinect. However, much of the interaction design research for such systems is still focused on desktop and touch interactions. Current thinking in free-space gestures are limited in capability and imagination and most gesture studies have not attempted to identify gestures appropriate for public walk-up-and-use applications. A walk-up-and-use display must be discoverable, such that first-time users can use the system without any training, flexible, and not fatiguing, especially in the case of longer-term interactions. One mechanism for defining gesture sets for walk-up-and-use interactions is a participatory design method called gesture elicitation. This method has been used to identify several user-generated gesture sets and shown that user-generated sets are preferred by users over those defined by system designers. However, for these studies to be successfully implemented in walk-up-and-use applications, there is a need to understand which components of these gestures are semantically meaningful (i.e. do users distinguish been using their left and right hand, or are those semantically the same thing?). Thus, defining a standardized gesture vocabulary for coding, characterizing, and evaluating gestures is critical. This dissertation presents three gesture elicitation studies for walk-up-and-use displays that employ a novel gesture elicitation methodology, alongside a novel coding scheme for gesture elicitation data that focuses on features most important to users’ mental models. Generalizable design principles, based on the three studies, are then derived and presented (e.g. changes in speed are meaningful for scroll actions in walk up and use displays but not for paging or selection). The major contributions of this work are: (1) an elicitation methodology that aids users in overcoming biases from existing interaction modalities; (2) a better understanding of the gestural features that matter, e.g. that capture the intent of the gestures; and (3) generalizable design principles for walk-up-and-use public displays.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Handling ambiguous user input on touchscreen kiosks

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 87-94).Touchscreen kiosks are becoming an increasingly popular means of providing a wide arrange of services to the public. However, the principal drawback of these types of systems lies within the elevated error rates due to finger imprecision and screen miscalibration. These issues become worrisome, considering the greater responsibilities and reliance placed upon touchscreens. This thesis investigates two novel techniques that attempt to alleviate these interaction problems. The first technique, predictive pointing, incorporates information regarding past interactions and an area cursor (which maps the user's touch to a circular area rather than a single point) to provide a better estimate of the intended selection. The second technique, gestural drawing, allows users to draw particular shapes onscreen to execute actions as an alternative means of input that is largely unaffected by issues of miscalibration. Results from a user study indicated that both techniques provided significant advantages in not only lowering error rates, but also improving task completion times over traditional tasks of target selection.by Christopher K. Leung.M.Eng

WatchTrace: Design and Evaluation of an At-Your-Side Gesture Paradigm

In this thesis, we present the exploration and evaluation of a gesture interaction paradigm performed with arms at rest at the side of one's body. This gesture stance is informed persisting challenges in mid-air arm gesture interactions in relation to fatigue and social acceptability. The proposed arms-down posture reduces physical effort by minimizing the shoulder torque placed on the user. While this interaction posture has been previously explored, the gesture vocabulary in previous research has been small and limited. The design of this gesture interaction is motivated by the ability to provide rich and expressive input; the user performs gestures by moving the whole arm at the side of the body to create two-dimensional visual traces, as in hand-drawing in a bounded plane parallel to the ground. Within this space, we present the results of two studies that investigate the use of side-gesture input for interaction. First, we explore the users' mental model for using this interaction by conducting an elicitation study on a set of everyday tasks one would perform on a large display in public to semi-public contexts. The takeaway from this study presents the need for a dynamic and expressive set of gesture vocabulary including ideographic and alphanumeric gesture constructs that can be combined or chained together. We then explore the feasibility of designing such a gesture recognition system using commodity hardware and recognition techniques, dubbed WatchTrace, which supports alphanumeric gestures of up to length three, providing a vibrant, dynamic, and feasible gestural vocabulary. Finally, we explore potential approaches to improve the recognition through the use of adaptive thresholds, n-best lists, and changing reject rates among other conventional techniques in the field of gesture classification

Contex-aware gestures for mixed-initiative text editings UIs

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Interacting with computers following peer review. The version of record is available online at: http://dx.doi.org/10.1093/iwc/iwu019[EN] This work is focused on enhancing highly interactive text-editing applications with gestures. Concretely, we study Computer Assisted Transcription of Text Images (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 text-editing interfaces, without worrying to verify the user intent on-screen. 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 (<1% error rate) with very high performance (<1 ms of recognition time). 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.This work has been supported by the European Commission through the 7th Framework Program (tranScriptorium: FP7- ICT-2011-9, project 600707 and CasMaCat: FP7-ICT-2011-7, project 287576). It has also been supported by the Spanish MINECO under grant TIN2012-37475-C02-01 (STraDa), and the Generalitat Valenciana under grant ISIC/2012/004 (AMIIS).Leiva, 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. https://doi.org/10.1093/iwc/iwu019S675696276Alabau V. Leiva L. A. Transcribing Handwritten Text Images with a Word Soup Game. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA) 2012.Alabau V. Rodríguez-Ruiz L. Sanchis A. Martínez-Gómez P. Casacuberta F. On Multimodal Interactive Machine Translation Using Speech Recognition. Proc. Int. Conf. Multimodal Interfaces (ICMI). 2011a.Alabau V. Sanchis A. Casacuberta F. Improving On-Line Handwritten Recognition using Translation Models in Multimodal Interactive Machine Translation. Proc. Assoc. Comput. Linguistics (ACL) 2011b.Alabau, V., Sanchis, A., & Casacuberta, F. (2014). Improving on-line handwritten recognition in interactive machine translation. Pattern Recognition, 47(3), 1217-1228. doi:10.1016/j.patcog.2013.09.035Anthony L. Wobbrock J. O. A Lightweight Multistroke Recognizer for User Interface Prototypes. Proc. Conf. Graph. Interface (GI). 2010.Anthony L. Wobbrock J. O. N-Protractor: a Fast and Accurate Multistroke Recognizer. Proc. Conf. Graph. Interface (GI) 2012.Anthony L. Vatavu R.-D. Wobbrock J. O. Understanding the Consistency of Users' Pen and Finger Stroke Gesture Articulation. Proc. Conf. Graph. Interface (GI). 2013.Appert C. Zhai S. Using Strokes as Command Shortcuts: Cognitive Benefits and Toolkit Support. Proc. SIGCHI Conf. Hum. Fact. Comput. Syst. (CHI) 2009.Bahlmann C. Haasdonk B. Burkhardt H. On-Line Handwriting Recognition with Support Vector Machines: A Kernel Approach. Proc. Int. Workshop Frontiers Handwriting Recognition (IWFHR). 2001.Bailly G. Lecolinet E. Nigay L. Flower Menus: a New Type of Marking Menu with Large Menu Breadth, within Groups and Efficient Expert Mode Memorization. Proc.Work. Conf. Adv. Vis. Interfaces (AVI) 2008.Balakrishnan R. Patel P. The PadMouse: Facilitating Selection and Spatial Positioning for the Non-Dominant Hand. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 1998.Bau O. Mackay W. E. Octopocus: A Dynamic Guide for Learning Gesture-Based Command Sets. Proc. ACM Symp. User Interface Softw. Technol. (UIST) 2008.Belaid A. Haton J. A syntactic approach for handwritten formula recognition. IEEE Trans. Pattern Anal. Mach. Intell. 1984;6:105-111.Bosch V. Bordes-Cabrera I. Munoz P. C. Hernández-Tornero C. Leiva L. A. Pastor M. Romero V. Toselli A. H. Vidal E. Transcribing a XVII Century Handwritten Botanical Specimen Book from Scratch. Proc. Int. Conf. Digital Access Textual Cultural Heritage (DATeCH). 2014.Buxton W. The natural language of interaction: a perspective on non-verbal dialogues. INFOR 1988;26:428-438.Cao X. Zhai S. Modeling Human Performance of Pen Stroke Gestures. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 2007.Castro-Bleda M. J. España-Boquera S. Llorens D. Marzal A. Prat F. Vilar J. M. Zamora-Martinez F. Speech Interaction in a Multimodal Tool for Handwritten Text Transcription. Proc. Int. Conf. Multimodal Interfaces (ICMI) 2011.Connell S. D. Jain A. K. Template-based on-line character recognition. Pattern Recognition 2000;34:1-14.Costagliola G. Deufemia V. Polese G. Risi M. A Parsing Technique for Sketch Recognition Systems. Proc. 2004 IEEE Symp. Vis. Lang. Hum. Centric Comput. (VLHCC). 2004.Culotta, A., Kristjansson, T., McCallum, A., & Viola, P. (2006). Corrective feedback and persistent learning for information extraction. Artificial Intelligence, 170(14-15), 1101-1122. doi:10.1016/j.artint.2006.08.001Deepu V. Madhvanath S. Ramakrishnan A. Principal Component Analysis for Online Handwritten Character Recognition. Proc. Int. Conf. Pattern Recognition (ICPR). 2004.Delaye A. Sekkal R. Anquetil E. Continuous Marking Menus for Learning Cursive Pen-Based Gestures. Proc. Int. Conf. Intell. User Interfaces (IUI) 2011.Dimitriadis Y. Coronado J. Towards an art-based mathematical editor that uses on-line handwritten symbol recognition. Pattern Recognition 1995;8:807-822.El Meseery M. El Din M. F. Mashali S. Fayek M. Darwish N. Sketch Recognition Using Particle Swarm Algorithms. Proc. 16th IEEE Int. Conf. Image Process. (ICIP). 2009.Goldberg D. Goodisman A. Stylus User Interfaces for Manipulating Text. Proc. ACM Symp. User Interface Softw. Technol. (UIST) 1991.Goldberg D. Richardson C. Touch-Typing with a Stylus. Proc. INTERCHI'93 Conf. Hum. Factors Comput. Syst. 1993.Stevens, M. E. (1968). Selected pattern recognition projects in Europe. Pattern Recognition, 1(2), 103-118. doi:10.1016/0031-3203(68)90002-2Hardock G. Design Issues for Line Driven Text Editing/ Annotation Systems. Proc. Conf. Graph. Interface (GI). 1991.Hardock G. Kurtenbach G. Buxton W. A Marking Based Interface for Collaborative Writing. Proc.ACM Symp. User Interface Softw. Technol. (UIST) 1993.Hinckley K. Baudisch P. Ramos G. Guimbretiere F. Design and Analysis of Delimiters for Selection-Action Pen Gesture Phrases in Scriboli. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 2005.Hong J. I. Landay J. A. SATIN: A Toolkit for Informal Ink-Based Applications. Proc. ACM Symp. User Interface Softw. Technol. (UIST) 2000.Horvitz E. Principles of Mixed-Initiative User Interfaces. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 1999.Huerst W. Yang J. Waibel A. Interactive Error Repair for an Online Handwriting Interface. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI) 2010.Jelinek F. Cambridge, Massachusetts: MIT Press; 1998. Statistical Methods for Speech Recognition.Johansson S. Atwell E. Garside R. Leech G. The Tagged LOB Corpus, User's Manual. Norwegian Computing Center for the Humanities. 1996.Karat C.-M. Halverson C. Horn D. Karat J. Patterns of Entry and Correction in Large Vocabulary Continuous Speech Recognition Systems. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 1999.Kerrick, D. D., & Bovik, A. C. (1988). Microprocessor-based recognition of handprinted characters from a tablet input. Pattern Recognition, 21(5), 525-537. doi:10.1016/0031-3203(88)90011-8Koschinski M. Winkler H. Lang M. Segmentation and Recognition of Symbols within Handwritten Mathematical Expressions. Proc. IEEE Int. Conf. Acoustics Speech Signal Process. (ICASSP). 1995.Kosmala A. Rigoll G. On-Line Handwritten Formula Recognition Using Statistical Methods. Proc. Int. Conf. Pattern Recognition (ICPR) 1998.Kristensson P. O. Discrete and continuous shape writing for text entry and control. 2007. Ph.D. Thesis, Linköping University, Sweden.Kristensson P. O. Denby L. C. Text Entry Performance of State of the Art Unconstrained Handwriting Recognition: a Longitudinal User Study. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 2009.Kristensson P. O. Denby L. C. Continuous Recognition and Visualization of Pen Strokes and Touch-Screen Gestures. Proc. Eighth Eurograph. Symp. Sketch-Based Interfaces Model. (SBIM) 2011.Kristensson P. O. Zhai S. SHARK2: A Large Vocabulary Shorthand Writing System for Pen-Based Computers. Proc. ACM Symp. User Interface Softw. Technol. (UIST). 2004.Kurtenbach G. P. The design and evaluation of marking menus. 1991. Ph.D. Thesis, University of Toronto.Kurtenbach G. P. Buxton W. Issues in Combining Marking and Direct Manipulation Techniques. Proc. ACM Symp. User Interface Softw. Technol. (UIST). 1991.Kurtenbach G. Buxton W. User Learning and Performance with Marking Menus. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA) 1994.Kurtenbach, G., Sellen, A., & Buxton, W. (1993). An Empirical Evaluation of Some Articulatory and Cognitive Aspects of Marking Menus. Human-Computer Interaction, 8(1), 1-23. doi:10.1207/s15327051hci0801_1LaLomia M. User Acceptance of Handwritten Recognition Accuracy. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA). 1994.Leiva L. A. Romero V. Toselli A. H. Vidal E. Evaluating an Interactive–Predictive Paradigm on Handwriting Transcription: A Case Study and Lessons Learned. Proc. 35th Annu. IEEE Comput. Softw. Appl. Conf. (COMPSAC) 2011.Leiva L. A. Alabau V. Vidal E. Error-Proof, High-Performance, and Context-Aware Gestures for Interactive Text Edition. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA). 2013.Li Y. Protractor: A Fast and Accurate Gesture Recognizer. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI) 2010.Li W. Hammond T. Using Scribble Gestures to Enhance Editing Behaviors of Sketch Recognition Systems. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA). 2012.Liao C. Guimbretière F. Hinckley K. Hollan J. Papiercraft: a gesture-based command system for interactive paper. ACM Trans. Comput.–Hum. Interaction (TOCHI) 2008;14:18:1-18:27.Liu P. Soong F. K. Word Graph Based Speech Rcognition Error Correction by Handwriting Input. Proc. Int. Conf. Multimodal Interfaces (ICMI). 2006.Long A. Landay J. Rowe L. Implications for a Gesture Design Tool. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI) 1999.Long A. C. Jr. Landay J. A. Rowe L. A. Michiels J. Visual Similarity of Pen Gestures. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 2000.MacKenzie, I. S., & Chang, L. (1999). A performance comparison of two handwriting recognizers. Interacting with Computers, 11(3), 283-297. doi:10.1016/s0953-5438(98)00030-7MacKenzie I. S. Tanaka-Ishii K. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.; 2007. Text Entry Systems: Mobility, Accessibility, Universality.MARTI, U.-V., & BUNKE, H. (2001). USING A STATISTICAL LANGUAGE MODEL TO IMPROVE THE PERFORMANCE OF AN HMM-BASED CURSIVE HANDWRITING RECOGNITION SYSTEM. International Journal of Pattern Recognition and Artificial Intelligence, 15(01), 65-90. doi:10.1142/s0218001401000848Marti, U.-V., & Bunke, H. (2002). The IAM-database: an English sentence database for offline handwriting recognition. International Journal on Document Analysis and Recognition, 5(1), 39-46. doi:10.1007/s100320200071Martín-Albo D. Romero V. Toselli A. H. Vidal E. Multimodal computer-assisted transcription of text images at character-level interaction. Int. J. Pattern Recogn. Artif. Intell. 2012;26:1-19.Marzinkewitsch R. Operating Computer Algebra Systems by Hand-Printed Input. Proc. Int. Symp. Symbolic Algebr. Comput. (ISSAC). 1991.Mas, J., Llados, J., Sanchez, G., & Jorge, J. A. P. (2010). A syntactic approach based on distortion-tolerant Adjacency Grammars and a spatial-directed parser to interpret sketched diagrams. Pattern Recognition, 43(12), 4148-4164. doi:10.1016/j.patcog.2010.07.003Moyle M. Cockburn A. Analysing Mouse and Pen Flick Gestures. Proc. SIGCHI-NZ Symp. Comput.–Hum. Interact. (CHINZ). 2002.Nakayama Y. A Prototype Pen-Input Mathematical Formula Editor. Proc. AACE EdMedia 1993.Ogata J. Goto M. Speech Repair: Quick Error Correction Just by Using Selection Operation for Speech Input Interface. Proc. Eurospeech. 2005.Ortiz-Martínez D. Leiva L. A. Alabau V. Casacuberta F. Interactive Machine Translation using a Web-Based Architecture. Proc. Int. Conf. Intell. User Interfaces (IUI) 2010.Ortiz-Martínez D. Leiva L. A. Alabau V. García-Varea I. Casacuberta F. An Interactive Machine Translation System with Online Learning. Proc. Assoc. Comput. Linguist. (ACL). 2011.Michael Powers, V. (1973). Pen direction sequences in character recognition. Pattern Recognition, 5(4), 291-302. doi:10.1016/0031-3203(73)90022-8Raab F. Extremely efficient menu selection: Marking menus for the Flash platform. 2009. Available at http://www.betriebsraum.de/blog/2009/07/21/efficient-gesture-recognition-and-corner-finding-in-as3/ (retrieved on May 2012).Revuelta-Martínez A. Rodríguez L. García-Varea I. A Computer Assisted Speech Transcription System. Proc. Eur. Chap. Assoc. Comput. Linguist. (EACL). 2012.Revuelta-Martínez, A., Rodríguez, L., García-Varea, I., & Montero, F. (2013). Multimodal interaction for information retrieval using natural language. Computer Standards & Interfaces, 35(5), 428-441. doi:10.1016/j.csi.2012.11.002Rodríguez L. García-Varea I. Revuelta-Martínez A. Vidal E. A Multimodal Interactive Text Generation System. Proc. Int. Conf. Multimodal Interfaces Workshop Mach. Learn. Multimodal Interact. (ICMI-MLMI). 2010a.Rodríguez L. García-Varea I. Vidal E. Multi-Modal Computer Assisted Speech Transcription. Proc. Int. Conf. Multimodal Interfaces Workshop Mach. Learn. Multimodal Interact. (ICMI-MLMI) 2010b.Romero V. Leiva L. A. Toselli A. H. Vidal E. Interactive Multimodal Transcription of Text Images using a Web-Based Demo System. Proc. Int. Conf. Intell. User Interfaces (IUI). 2009a.Romero V. Toselli A. H. Vidal E. Using Mouse Feedback in Computer Assisted Transcription of Handwritten Text Images. Proc. Int. Conf. Doc. Anal. Recogn. (ICDAR) 2009b.Romero V. Toselli A. H. Vidal E. Study of Different Interactive Editing Operations in an Assisted Transcription System. Proc. Int. Conf. Multimodal Interfaces (ICMI). 2011.Romero V. Toselli A. H. Vidal E. Vol. 80. Singapore: World Scientific Publishing Company; 2012. Multimodal Interactive Handwritten Text Transcription.Rubine, D. (1991). Specifying gestures by example. ACM SIGGRAPH Computer Graphics, 25(4), 329-337. doi:10.1145/127719.122753Rubine D. H. 1991b. The automatic recognition of gestures. Ph.D. Thesis, Carnegie Mellon University.Sánchez-Sáez R. Leiva L. A. Sánchez J. A. Benedí J. M. Interactive Predictive Parsing using a Web-Based Architecture. Proc. North Am. Chap. Assoc. Comput. Linguist. 2010.Saund E. Fleet D. Larner D. Mahoney J. Perceptually-Supported Image Editing of Text and Graphics. Proc. ACM Symp. User Interface Softw. Technol. (UIST) 2003.Shilman M. Tan D. S. Simard P. CueTIP: a Mixed-Initiative Interface for Correcting Handwriting Errors. Proc. ACM Symp. User Interface Softw. Technol. (UIST). 2006.Signer B. Kurmann U. Norrie M. C. igesture: A General Gesture Recognition Framework. Proc. Int. Conf. Doc. Anal. Recogn. (ICDAR) 2007.Smithies S. Novins K. Arvo J. A handwriting-based equation editor. Proc. Conf. Graph. Interface (GI). 1999.Suhm, B., Myers, B., & Waibel, A. (2001). Multimodal error correction for speech user interfaces. ACM Transactions on Computer-Human Interaction, 8(1), 60-98. doi:10.1145/371127.371166Tappert C. C. Mosley P. H. Recent advances in pen computing. 2001. Technical Report 166, Pace University, available: http://support.csis.pace.edu.Toselli, A. H., Romero, V., Pastor, M., & Vidal, E. (2010). Multimodal interactive transcription of text images. Pattern Recognition, 43(5), 1814-1825. doi:10.1016/j.patcog.2009.11.019Toselli A. H. Vidal E. Casacuberta F. , editors. Berlin, Heidelberg, New York: Springer; 2011. Multimodal-Interactive Pattern Recognition and Applications.Tseng S. Fogg B. Credibility and computing technology. Commun. ACM 1999;42:39-44.Vatavu R.-D. Anthony L. Wobbrock J. O. Gestures as Point Clouds: A P Recognizer for User Interface Prototypes. Proc. Int. Conf. Multimodal Interfaces (ICMI). 2012.Vertanen K. Kristensson P. O. Parakeet: A Continuous Speech Recognition System for Mobile Touch-Screen Devices. Proc. Int. Conf. Intell. User Interfaces (IUI) 2009.Vidal E. Rodríguez L. Casacuberta F. García-Varea I. Mach. Learn. Multimodal Interact., Lect. Notes Comput. Sci. Vol. 4892. Berlin, Heidelberg: Springer; 2008. Interactive Pattern Recognition.Wang X. Li J. Ao X. Wang G. Dai G. Multimodal Error Correction for Continuous Handwriting Recognition in Pen-Based User Interfaces. Proc. Int. Conf. Intell. User Interfaces (IUI). 2006.Wang L. Hu T. Liu P. Soong F. K. Efficient Handwriting Correction of Speech Recognition Errors with Template Constrained Posterior (TCP). Proc. INTERSPEECH 2008.Wobbrock J. O. Wilson A. D. Li Y. Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes. Proc. ACM Symp. User Interface Softw. Technol. (UIST). 2007.Wolf C. G. Morrel-Samuels P. The use of hand-drawn gestures for text editing. Int. J. Man–Mach. Stud. 1987;27:91-102.Zeleznik R. Miller T. Fluid Inking: Augmenting the Medium of Free-Form Inking with Gestures. Proc. Conf. Graph. Interface (GI). 2006.Yong Zhang, McCullough, C., Sullins, J. R., & Ross, C. R. (2010). Hand-Drawn Face Sketch Recognition by Humans and a PCA-Based Algorithm for Forensic Applications. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 40(3), 475-485. doi:10.1109/tsmca.2010.2041654Zhao S. Balakrishnan R. Simple vs. Compound Mark Hierarchical Marking Menus. Proc. ACM Symp. User Interface Softw. Technol. (UIST) 2004

Investigating the characteristics of unistroke gestures using a mobile game

Touch gestures are today’s main input method for the interaction with smart phones. In particular, unistroke gestures, which are gestures consisting of one articulated line, are commonly used for text input (touch keyboards) and short cuts referring to functions on mobile devices. This work investigates the user’s accuracy of articulating unistroke touch gestures on mobile devices. Therefore, two studies were conducted which focused on the user’s ability to perceive different variations of unistroke gestures and reproduce them accurately. First, a control study aimed at analyzing the user’s touch accuracy during the articulation of single line and composed line gestures by varying gesture properties like the rotation or angles within the gestures. To analyze the infl uences of the user’s distraction in his natural environment, a large scale study was conducted which used a mobile game as apparatus. The game was designed in a way that the user was motivated to articulate the given gestures precisely. The game principle was based on the control study procedure of perceiving and reproducing gestures. To gather a great amount of touch samples the game was published on a mobile app store and the samples were collected trough the internet. The analysis of both studies showed that the gestures orientation and the angles within the gestures affected the articulation accuracy in terms of the deviations made by bending, rotating, and varying the shape of the gestures. Furthermore, the gestures articulated in the game study tended to be more error-prone compared to those being articulated in the control study.Touch-Gesten sind momentan die am weitesten verbreitete Eingabemethode für die Interaktion mit Smartphones. Insbesondere Unistroke-Gesten, welche aus einem einzelnen Strich bestehen, werden für die Texteingabe mittels Touch-Keyboards und als Shortcut für Funktion auf mobilen Geräten verwendet. Im Rahmen dieser Arbeit wird die Genauigkeit untersucht, mit der der Nutzer Unistroke-Gesten auf mobilen Geräten ausführt. Zu diesem Zweck wurden zwei Studien durchgeführt, die die Fähigkeit des Nutzers untersuchen, verschiedene vorgegebene Gesten wahrzunehmen und diese zu reproduzieren. Eine Kontroll-Studie zielte darauf ab zu untersuchen, wie genau der Nutzer Gesten wiedergeben kann, die aus einzelnen geraden Linien oder aus zusammengesetzten geraden Linien bestehen wobei die Winkel und die Längen dieser Linien in dem Experiment variiert wurden. Um die Ablenkung durch äußeren Einflüsse im gewohnten Nutzerumfeld zu untersuchen, wurde eine größer angelegte Studie mittels eines Mobile-Games durchgeführt. Das Spiel wurde so entworfen, dass der Nutzer motiviert war die vorgegebenen Gesten möglichst genau durchzuführen. Basis für das Spielprinzip war die Kontroll-Studie, bei der die Probanden die Gesten wahrnehmen und reproduzieren mussten. Um eine große Menge an Touch-Proben zu sammeln wurde das Spiel in einem App-Store für mobile Geräte veröffentlicht und die Ergebnisse über das Internet eingesammelt. Die Analyse beider Studien (Kontroll-Studie und Spiel-Studie) ergab, dass die Rotation und die Winkel innerhalb einer Geste eine Auswirkung auf die Genauigkeit der Ausführung haben. Dieser Effekt wurde an Variationen in der Biegung, der Winkel und der Form der Gesten beobachtet. Dabei wurden die Gesten von den Nutzern des Spiels (d.h. in der Spiel-Studie) ungenauer durchgeführt als in der Kontroll-Studie