Indic Scripts Based Online Form Filling - a Usability Exploration
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Motion and Context Sensing Techniques for Pen Computing
Motion and Context Sensing Techniques for Pen Computing Ken Hinckley1, Xiang ‘Anthony’ Chen1,2, and Hrvoje Benko1 * Microsoft Research, Redmond, WA, USA1 and Carnegie Mellon University Dept. of Computer Science2 ABSTRACT We explore techniques for a slender and untethered stylus prototype enhanced with a full suite of inertial sensors (three-axis accelerometer, gyroscope, and magnetometer). We present a taxonomy of enhanced stylus input techniques and consider a number of novel possibilities that combine motion sensors with pen stroke and touchscreen inputs on a pen + touch slate. These Fig. 1 Our wireless prototype has accelerometer, gyro, and inertial sensors enable motion-gesture inputs, as well sensing the magnetometer sensors in a ~19 cm Χ 11.5 mm diameter stylus. context of how the user is holding or using the stylus, even when Our system employs a custom pen augmented with inertial the pen is not in contact with the tablet screen. Our initial results sensors (accelerometer, gyro, and magnetometer, each a 3-axis suggest that sensor-enhanced stylus input offers a potentially rich sensor, for nine total sensing dimensions) as well as a low-power modality to augment interaction with slate computers. radio. Our stylus prototype also thus supports fully untethered Keywords: Stylus, motion sensing, sensors, pen+touch, pen input operation in a slender profile with no protrusions (Fig. 1). This allows us to explore numerous interactive possibilities that were Index Terms: H.5.2 Information Interfaces & Presentation: Input cumbersome in previous systems: our prototype supports direct input on tablet displays, allows pen tilting and other motions far 1 INTRODUCTION from the digitizer, and uses a thin, light, and wireless stylus. -
The Festvox Indic Frontend for Grapheme-To-Phoneme Conversion
The Festvox Indic Frontend for Grapheme-to-Phoneme Conversion Alok Parlikar, Sunayana Sitaram, Andrew Wilkinson and Alan W Black Carnegie Mellon University Pittsburgh, USA aup, ssitaram, aewilkin, [email protected] Abstract Text-to-Speech (TTS) systems convert text into phonetic pronunciations which are then processed by Acoustic Models. TTS frontends typically include text processing, lexical lookup and Grapheme-to-Phoneme (g2p) conversion stages. This paper describes the design and implementation of the Indic frontend, which provides explicit support for many major Indian languages, along with a unified framework with easy extensibility for other Indian languages. The Indic frontend handles many phenomena common to Indian languages such as schwa deletion, contextual nasalization, and voicing. It also handles multi-script synthesis between various Indian-language scripts and English. We describe experiments comparing the quality of TTS systems built using the Indic frontend to grapheme-based systems. While this frontend was designed keeping TTS in mind, it can also be used as a general g2p system for Automatic Speech Recognition. Keywords: speech synthesis, Indian language resources, pronunciation 1. Introduction in models of the spectrum and the prosody. Another prob- lem with this approach is that since each grapheme maps Intelligible and natural-sounding Text-to-Speech to a single “phoneme” in all contexts, this technique does (TTS) systems exist for a number of languages of the world not work well in the case of languages that have pronun- today. However, for low-resource, high-population lan- ciation ambiguities. We refer to this technique as “Raw guages, such as languages of the Indian subcontinent, there Graphemes.” are very few high-quality TTS systems available. -
An Empirical Study in Pen-Centric User Interfaces: Diagramming
EUROGRAPHICS Workshop on Sketch-Based Interfaces and Modeling (2008) C. Alvarado and M.- P. Cani (Editors) An Empirical Study in Pen-Centric User Interfaces: Diagramming Andrew S. Forsberg1, Andrew Bragdon1, Joseph J. LaViola Jr.2, Sashi Raghupathy3, Robert C. Zeleznik1 1Brown University, Providence, RI, USA 2University of Central Florida, Orlando, FL, USA 3Microsoft Corporation, Redmond, WA, USA Abstract We present a user study aimed at helping understand the applicability of pen-computing in desktop environments. The study applied three mouse-and-keyboard-based and three pen-based interaction techniques to six variations of a diagramming task. We ran 18 subjects from a general population and the key finding was that while the mouse and keyboard techniques generally were comparable or faster than the pen techniques, subjects ranked pen techniques higher and enjoyed them more. Our contribution is the results from a formal user study that suggests there is a broader applicability and subjective preference for pen user interfaces than the niche PDA and mobile market they currently serve. Categories and Subject Descriptors (according to ACM CCS): H.5.2 [User Interfaces]: Evaluation/Methodology 1. Introduction ficially appears pen-centric, users will in fact derive a sig- nificant benefit from using a pen-based interface. Our ap- Research on pen computing can be traced back at least to proach is to quantify formally, through head-to-head evalua- the early 60’s. Curiously though, there is little formal un- tion, user performance and relative preference for a represen- derstanding of when, where, and for whom pen comput- tative sampling of both keyboard and mouse, and pen-based ing is the user interface of choice. -
Tai Lü / ᦺᦑᦟᦹᧉ Tai Lùe Romanization: KNAB 2012
Institute of the Estonian Language KNAB: Place Names Database 2012-10-11 Tai Lü / ᦺᦑᦟᦹᧉ Tai Lùe romanization: KNAB 2012 I. Consonant characters 1 ᦀ ’a 13 ᦌ sa 25 ᦘ pha 37 ᦤ da A 2 ᦁ a 14 ᦍ ya 26 ᦙ ma 38 ᦥ ba A 3 ᦂ k’a 15 ᦎ t’a 27 ᦚ f’a 39 ᦦ kw’a 4 ᦃ kh’a 16 ᦏ th’a 28 ᦛ v’a 40 ᦧ khw’a 5 ᦄ ng’a 17 ᦐ n’a 29 ᦜ l’a 41 ᦨ kwa 6 ᦅ ka 18 ᦑ ta 30 ᦝ fa 42 ᦩ khwa A 7 ᦆ kha 19 ᦒ tha 31 ᦞ va 43 ᦪ sw’a A A 8 ᦇ nga 20 ᦓ na 32 ᦟ la 44 ᦫ swa 9 ᦈ ts’a 21 ᦔ p’a 33 ᦠ h’a 45 ᧞ lae A 10 ᦉ s’a 22 ᦕ ph’a 34 ᦡ d’a 46 ᧟ laew A 11 ᦊ y’a 23 ᦖ m’a 35 ᦢ b’a 12 ᦋ tsa 24 ᦗ pa 36 ᦣ ha A Syllable-final forms of these characters: ᧅ -k, ᧂ -ng, ᧃ -n, ᧄ -m, ᧁ -u, ᧆ -d, ᧇ -b. See also Note D to Table II. II. Vowel characters (ᦀ stands for any consonant character) C 1 ᦀ a 6 ᦀᦴ u 11 ᦀᦹ ue 16 ᦀᦽ oi A 2 ᦰ ( ) 7 ᦵᦀ e 12 ᦵᦀᦲ oe 17 ᦀᦾ awy 3 ᦀᦱ aa 8 ᦶᦀ ae 13 ᦺᦀ ai 18 ᦀᦿ uei 4 ᦀᦲ i 9 ᦷᦀ o 14 ᦀᦻ aai 19 ᦀᧀ oei B D 5 ᦀᦳ ŭ,u 10 ᦀᦸ aw 15 ᦀᦼ ui A Indicates vowel shortness in the following cases: ᦀᦲᦰ ĭ [i], ᦵᦀᦰ ĕ [e], ᦶᦀᦰ ăe [ ∎ ], ᦷᦀᦰ ŏ [o], ᦀᦸᦰ ăw [ ], ᦀᦹᦰ ŭe [ ɯ ], ᦵᦀᦲᦰ ŏe [ ]. -
Pen Interfaces
Understanding the Pen Input Modality Presented at the Workshop on W3C MMI Architecture and Interfaces Nov 17, 2007 Sriganesh “Sri-G” Madhvanath Hewlett-Packard Labs, Bangalore, India [email protected] © 2006 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice Objective • Briefly describe different aspects of pen input • Provide some food for thought … Nov 17, 2007 Workshop on W3C MMI Architecture and Interfaces Unimodal input in the context of Multimodal Interfaces • Multimodal interfaces are frequently used unimodally − Based on • perceived suitability of modality to task • User experience, expertise and preference • It is important that a multimodal interface provide full support for individual modalities − “Multimodality” cannot be a substitute for incomplete/immature support for individual modalities Nov 17, 2007 Workshop on W3C MMI Architecture and Interfaces Pen Computing • Very long history … predates most other input modalities − Light pen was invented in 1957, mouse in 1963 ! • Several well-studied aspects: − Hardware − Interface − Handwriting recognition − Applications • Many famous failures (Go, Newton, CrossPad) • Enjoying resurgence since 90s because of PDAs and TabletPCs − New technologies such as Digital Paper (e.g. Anoto) and Touch allow more natural and “wow” experiences Nov 17, 2007 Workshop on W3C MMI Architecture and Interfaces Pen/Digitizer Hardware … • Objective: Detect pen position, maybe more • Various technologies with own limitations and characteristics (and new ones still being developed !) − Passive stylus • Touchscreens on PDAs, some tablets • Capacitive touchpads on laptops (Synaptics) • Vision techniques • IR sensors in bezel (NextWindow) − Active stylus • IR + ultrasonic (Pegasus, Mimeo) • Electromagnetic (Wacom) • Camera in pen tip & dots on paper (Anoto) • Wide variation in form − Scale: mobile phone to whiteboard (e.g. -
Handwriting Recognition Systems: an Overview
Handwriting Recognition Systems: An Overview Avi Drissman Dr. Sethi CSC 496 February 26, 1997 Drissman 1 Committing words to paper in handwriting is a uniquely human act, performed daily by millions of people. If you were to present the idea of “decoding” handwriting to most people, perhaps the first idea to spring to mind would be graphology, which is the analysis of handwriting to determine its authenticity (or perhaps also the more non-scientific determination of some psychological character traits of the writer). But the more mundane, and more frequently overlooked, “decoding” of handwriting is handwriting recognition—the process of figuring out what words and letters the scribbles and scrawls on the paper represent. Handwriting recognition is far from easy. A common complaint and excuse of people is that they couldn’t read their own handwriting. That makes us ask ourselves the question: If people sometimes can’t read their own handwriting, with which they are quite familiar, what chance does a computer have? Fortunately, there are powerful tools that can be used that are easily implementable on a computer. A very useful one for handwriting recognition, and one that is used in several recognizers, is a neural network. Neural networks are richly connected networks of simple computational elements. The fundamental tenet of neural computation (or computation with [neural networks]) is that such networks can carry out complex cognitive and computational tasks. [9] In addition, one of the tasks at which neural networks excel is the classification of input data into one of several groups or categories. This ability is one of the main reasons neural networks are used for this purpose. -
Pen Computing History
TheThe PastPast andand FutureFuture ofof PenPen ComputingComputing Conrad H. Blickenstorfer, Editor-in-Chief Pen Computing Magazine [email protected] http://www.pencomputing.com ToTo buildbuild thethe future,future, wewe mustmust learnlearn fromfrom thethe pastpast HistoryHistory ofof penpen computingcomputing 1914: Goldberg gets US patent for recognition of handwritten numbers to control machines 1938: Hansel gets US patent for machine recognition of handwriting 1956: RAND Corporation develops digitizing tablet for handwriting recognition 1957-62: Handwriting recognition projects with accuracies of 97-99% 1963: Bell Labs develops cursive recognizer 1966: RAND creates GRAIL, similar to Graffiti Pioneer:Pioneer: AlanAlan KayKay Utah State University Stanford University Xerox PARC: GUI, SmallTalk, OOL Apple Computer Research Fellow Disney Envisioned Dynabook in 1968: The Dynabook will be a “dynamic medium for creative thought, capable of synthesizing all media – pictures, animation, sound, and text – through the intimacy and responsiveness of the personal computer.” HistoryHistory ofof penpen computingcomputing 1970s: Commercial products, including kana/romanji billing machine 1980s: Handwriting recognition companies – Nestor – Communication Intelligence Corporation – Lexicus – Several others Pioneers:Pioneers: AppleApple 1987 Apple prototype – Speech recognition – Intelligent agents – Camera – Folding display – Video conferencing – Wireless communication – Personal Information Manager ““KnowledgeKnowledge NavigatorNavigator”” -
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Phonotactic frequencies in Marathi KELLY HARPER BERKSON1 and MAX NELSON Indiana University Bloomington, Indiana ABSTRACT: Breathy sonorants are cross-linguistically rare, occurring in just 1% of the languages indexed in the UCLA Phonological Segment Inventory Database (UPSID) and 0.2% of those in the PHOIBLE database (Moran, McCloy and Wright 2014). Prior work has shed some light on their acoustic properties, but little work has investigated the language-internal distribution of these sounds in a language where they do occur, such as Marathi (Indic, spoken mainly in Maharashtra, India). With this in mind, we present an overview of the phonotactic frequencies of consonants, vowels, and CV-bigrams in the Marathi portion of the EMILLE/CIIL corpus. Results of a descriptive analysis show that breathy sonorants are underrepresented, making up fewer than 1% of the consonants in the 2.2 million-word corpus, and that they are disfavored in back vowel contexts. 1 Introduction Languages are rife with gradience. While some sounds, morphological patterns, and syntactic structures occur with great frequency in a language, others—though legal—occur rarely. The same is observed crosslinguistically: some elements and structures are found in language after language, while others are quite rare. For at least the last several decades, cognitive and psycholinguistic investigation of language processing has revealed that language users are sensitive not only to categorical phonological knowledge—such as, for example, the sounds and sound combinations that are legal and illegal in one’s language— but also to this kind of gradient information about phonotactic probabilities. This has been demonstrated in a variety of experimental tasks (Ellis 2002; Frisch, Large, and Pisoni 2000; Storkel and Maekawa 2005; Vitevitch and Luce 2005), which indicate that gradience influences language production, comprehension, and processing phenomena (Ellis 2002; Vitevitch, Armbrüster, and Chu 2004) for infants as well as adults (Jusczyk and Luce 1994). -
Neural Machine Translation System of Indic Languages - an Attention Based Approach
Neural Machine Translation System of Indic Languages - An Attention based Approach Parth Shah Vishvajit Bakrola Uka Tarsadia University Uka Tarsadia University Bardoli, India Bardoli, India [email protected] [email protected] Abstract—Neural machine translation (NMT) is a recent intervention. This task can be effectively done using machine and effective technique which led to remarkable improvements translation. in comparison of conventional machine translation techniques. Machine Translation (MT) is described as a task of compu- Proposed neural machine translation model developed for the Gujarati language contains encoder-decoder with attention mech- tationally translate human spoken or natural language text or anism. In India, almost all the languages are originated from their speech from one language to another with minimum human ancestral language - Sanskrit. They are having inevitable similari- intervention. Machine translation aims to generate translations ties including lexical and named entity similarity. Translating into which have the same meaning as a source sentence and Indic languages is always be a challenging task. In this paper, we grammatically correct in the target language. Initial work on have presented the neural machine translation system (NMT) that can efficiently translate Indic languages like Hindi and Gujarati MT started in early 1950s [2], and has advanced rapidly that together covers more than 58.49 percentage of total speakers since the 1990s due to the availability of more computational in the country. We have compared the performance of our capacity and training data. Then after, number of approaches NMT model with automatic evaluation matrices such as BLEU, have been proposed to achieve more and more accurate ma- perplexity and TER matrix. -
An Introduction to Indic Scripts
An Introduction to Indic Scripts Richard Ishida W3C [email protected] HTML version: http://www.w3.org/2002/Talks/09-ri-indic/indic-paper.html PDF version: http://www.w3.org/2002/Talks/09-ri-indic/indic-paper.pdf Introduction This paper provides an introduction to the major Indic scripts used on the Indian mainland. Those addressed in this paper include specifically Bengali, Devanagari, Gujarati, Gurmukhi, Kannada, Malayalam, Oriya, Tamil, and Telugu. I have used XHTML encoded in UTF-8 for the base version of this paper. Most of the XHTML file can be viewed if you are running Windows XP with all associated Indic font and rendering support, and the Arial Unicode MS font. For examples that require complex rendering in scripts not yet supported by this configuration, such as Bengali, Oriya, and Malayalam, I have used non- Unicode fonts supplied with Gamma's Unitype. To view all fonts as intended without the above you can view the PDF file whose URL is given above. Although the Indic scripts are often described as similar, there is a large amount of variation at the detailed implementation level. To provide a detailed account of how each Indic script implements particular features on a letter by letter basis would require too much time and space for the task at hand. Nevertheless, despite the detail variations, the basic mechanisms are to a large extent the same, and at the general level there is a great deal of similarity between these scripts. It is certainly possible to structure a discussion of the relevant features along the same lines for each of the scripts in the set. -
Pen Computer Technology
Pen Computer Technology Educates the reader about the technologies involved in a pen computer Fujitsu PC Corporation www.fujitsupc.com For more information: [email protected] © 2002 Fujitsu PC Corporation. All rights reserved. This paper is intended to educate the reader about the technologies involved in a pen computer. After reading this paper, the reader should be better equipped to make intelligent purchasing decisions about pen computers. Types of Pen Computers In this white paper, "pen computer" refers to a portable computer that supports a pen as a user interface device, and whose LCD screen measures at least six inches diagonally. This product definition encompasses five generally recognized categories of standard products, listed in Table 1 below. PRODUCT TARGET PC USER STORAGE OPERATING RUNS LOCAL EXAMPLE CATEGORY MARKET INTERFACE SYSTEM PROGRAMS Webpad Consumer & No Standard Flash Windows CE, Only via Honeywell Enterprise browser memory Linux, QNX browser WebPAD II plug-ins CE Tablet Enterprise No Specialized Flash Windows CE Yes Fujitsu applications memory PenCentra Pen Tablet Enterprise Yes Windows & Hard drive Windows 9x, Yes Fujitsu specialized NT-4, 2000, Stylistic applications XP Pen-Enabled Consumer Yes Windows Hard drive Windows 9x, Yes Fujitsu & Enterprise 2000, XP LifeBook B Series Tablet PC Consumer Yes Windows Hard drive Windows XP Yes Many under & Enterprise Tablet PC development Edition Table 1: Categories of Pen Computers with LCD Displays of Six Inches or Larger Since the different types of pen computers are often confused, the following paragraphs are intended to help explain the key distinguishing characteristics of each product category. Pen Computers Contrasted Webpad: A Webpad's primary characteristic is that its only user interface is a Web browser. -
Enabling Freehand Sketching Through Improved Primitive Recognition
RETHINKING PEN INPUT INTERACTION: ENABLING FREEHAND SKETCHING THROUGH IMPROVED PRIMITIVE RECOGNITION A Dissertation by BRANDON CHASE PAULSON Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY May 2010 Major Subject: Computer Science RETHINKING PEN INPUT INTERACTION: ENABLING FREEHAND SKETCHING THROUGH IMPROVED PRIMITIVE RECOGNITION A Dissertation by BRANDON CHASE PAULSON Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Approved by: Chair of Committee, Tracy Hammond Committee Members, Yoonsuck Choe Ricardo Gutierrez-Osuna Vinod Srinivasan Head of Department, Valerie E. Taylor May 2010 Major Subject: Computer Science iii ABSTRACT Rethinking Pen Input Interaction: Enabling Freehand Sketching Through Improved Primitive Recognition. (May 2010) Brandon Chase Paulson, B.S., Baylor University Chair of Advisory Committee: Dr. Tracy Hammond Online sketch recognition uses machine learning and artificial intelligence tech- niques to interpret markings made by users via an electronic stylus or pen. The goal of sketch recognition is to understand the intention and meaning of a partic- ular user's drawing. Diagramming applications have been the primary beneficiaries of sketch recognition technology, as it is commonplace for the users of these tools to first create a rough sketch of a diagram on paper before translating it into a machine understandable model, using computer-aided design tools, which can then be used to perform simulations or other meaningful tasks. Traditional methods for performing sketch recognition can be broken down into three distinct categories: appearance-based, gesture-based, and geometric-based.