On-line cursive script recognition: A user-adaptive system for word identification

Duneau, L.; Dorizzi, Bernadette

doi:10.1016/s0031-3203(96)00042-8

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…This review was not intended to be comprehensive. Indeed, end-user interactive machine learning is also used in other domains such as human-robot interaction (e.g., Goodrich and Schultz, 2007;Nicolescu and Mataric, 2001)) and recognition systems (e.g., Daneu and Dorizzi, 1996;Licsar and Sziranyi, 2005)). This review instead serves to illustrate the diversity of interesting uses of end-user interactive machine learning as well as to provide a reference for designing future systems.…”

Section: New Uses Of End-user Interactive Machine Learningmentioning

confidence: 99%

Designing for effective end-user interaction with machine learning

Amershi

2011

Proceedings of the 24th Annual ACM Symposium Adjunct on User Interface Software and Technology

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Designing for Effective End-User Interaction with Machine Learning Saleema AmershiChair of the Supervisory Committee:Associate Professor James A. Fogarty Computer Science & EngineeringEnd-user interactive machine learning is a promising tool for enhancing human capabilities with data.Recent work has shown that we can create specific applications that employ end-user interactive machine learning. However, we still lack a generalized understanding of how to design effective end-user interaction with machine learning. This dissertation advances our understanding of this problem by demonstrating effective end-user interaction with machine learning in a variety of new situations and by characterizing the design factors affecting the end-user interactive machine learning process itself.Specifically, this dissertation presents (1) new interaction techniques for end-user creation of image classifiers in an existing end-user interactive machine learning system called CueFlik, (2) a novel system called ReGroup that employs end-user interactive machine learning for the purpose of access control in social networks, (3) a novel system called CueT that supports end-user driven machine learning for computer network alarm triage, and (4) a novel design space characterizing the goals and constraints impacting the end-user interactive machine learning process itself. Together, these contributions can move us beyond ad-hoc designs for specific applications and provide a foundation for future researchers and developers of end-user interactive machine learning systems.i

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Section: New Uses Of End-user Interactive Machine Learningmentioning

confidence: 99%

Designing for effective end-user interaction with machine learning

Amershi

2011

Proceedings of the 24th Annual ACM Symposium Adjunct on User Interface Software and Technology

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“…We used the same databases as Duneau [17], in which words had been written on a digitizing tablet sampling the pen trajectory at the frequency of 200 points per second. On the other hand, a fixed scale was imposed on the writer: letters without vertical extensions should be written between two fixed middle lines, and vertical upper and lower extensions should go far beyond these lines.…”

Section: Feature Extraction On the Input Drawingmentioning

confidence: 99%

Maximum mutual information training for an online neural predictive handwritten word recognition system

Garcia-Salicetti

Dorizzi²,

Gallinari

et al. 2001

IJDAR

Self Cite

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In this paper, we present a hybrid online handwriting recognition system based on hidden Markov models (HMMs). It is devoted to word recognition using large vocabularies. An adaptive segmentation of words into letters is integrated with recognition, and is at the heart of the training phase. A word-model is a left-right HMM in which each state is a predictive multilayer perceptron that performs local regression on the drawing (i.e., the written word) relying on a context of observations. A discriminative training paradigm related to maximum mutual information is used, and its potential is shown on a database of 9,781 words.

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Modelizing character allographs in omni-scriptor frame: a new non-supervised clustering algorithm

Prévost

Milgram

2000

Pattern Recognition Letters

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On-line cursive script recognition: A user-adaptive system for word identification

Cited by 7 publications

References 8 publications

Designing for effective end-user interaction with machine learning

Designing for effective end-user interaction with machine learning

Maximum mutual information training for an online neural predictive handwritten word recognition system

Modelizing character allographs in omni-scriptor frame: a new non-supervised clustering algorithm

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