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UIST '91 Proceedings of the 4th annual ACM symposium on User interface software and technology
Statistical methods for speech recognition
Statistical methods for speech recognition
Interactive error repair for an online handwriting interface
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Principles of mixed-initiative user interfaces
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
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Taming recognition errors with a multimodal interface
Communications of the ACM
A handwriting-based equation editor
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UIST '00 Proceedings of the 13th annual ACM symposium on User interface software and technology
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CueFlik: interactive concept learning in image search
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GI '08 Proceedings of graphics interface 2008
VoiceLabel: using speech to label mobile sensor data
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
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Proceedings of the 22nd annual ACM symposium on User interface software and technology
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Proceedings of the 15th international conference on Intelligent user interfaces
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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CHI '12 Extended Abstracts on Human Factors in Computing Systems
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CHI '13 Extended Abstracts on Human Factors in Computing Systems
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Journal of Ambient Intelligence and Smart Environments - Home-based Health and Wellness Measurement and Monitoring
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With advances in pen-based computing devices, handwriting has become an increasingly popular input modality. Researchers have put considerable effort into building intelligent recognition systems that can translate handwriting to text with increasing accuracy. However, handwritten input is inherently ambiguous, and these systems will always make errors. Unfortunately, work on error recovery mechanisms has mainly focused on interface innovations that allow users to manually transform the erroneous recognition result into the intended one. In our work, we propose a mixed-initiative approach to error correction. We describe CueTIP, a novel correction interface that takes advantage of the recognizer to continually evolve its results using the additional information from user corrections. This significantly reduces the number of actions required to reach the intended result. We present a user study showing that CueTIP is more efficient and better preferred for correcting handwriting recognition errors. Grounded in the discussion of CueTIP, we also present design principles that may be applied to mixed-initiative correction interfaces in other domains.